ALL TERRAIN

SVM n=100

##  [1] "Prediction error at end is:  0.787864153294906"
##  [2] "Prediction error at end is:  0.673634262865452"
##  [3] "Prediction error at end is:  0.581855948486265"
##  [4] "Prediction error at end is:  0.506383393051877"
##  [5] "Prediction error at end is:  0.467109362829101"
##  [6] "Prediction error at end is:  0.441621228644129"
##  [7] "Prediction error at end is:  0.427339409046061"
##  [8] "Prediction error at end is:  0.424797476242405"
##  [9] "Prediction error at end is:  0.411534766578387"
## [10] "Prediction error at end is:  0.403897284104482"
##                                 k 1                              k 2
## 1           ChannelNetworkBaseLevel       Channel_Network_Base_Level
## 2                           Texture            slope_DTM_50m_avg_ws7
## 3             slope_DTM_50m_avg_ws7                          Texture
## 4           Modified_Catchment_Area          ChannelNetworkBaseLevel
## 5        Channel_Network_Base_Level               Closed_Depressions
## 6             profc_DTM_50m_avg_ws7                    TPI_i0m_o500m
## 7                   Catchment_slope        Topographic_Wetness_Index
## 8                         slope_ws7 VerticalDistancetoChannelNetwork
## 9                         Convexity                        Convexity
## 10 VerticalDistancetoChannelNetwork                  Catchment_slope
##                                 k 3                               k 4
## 1        Channel_Network_Base_Level             slope_DTM_50m_avg_ws3
## 2             slope_DTM_50m_avg_ws7        Channel_Network_Base_Level
## 3                           Texture                           Texture
## 4           ChannelNetworkBaseLevel           ChannelNetworkBaseLevel
## 5           Modified_Catchment_Area                     TPI_i0m_o500m
## 6  VerticalDistancetoChannelNetwork saga_Topographic_Wetness_Index_hr
## 7                   Catchment_slope                   Catchment_slope
## 8         Topographic_Wetness_Index             longc_DTM_50m_avg_ws7
## 9                     TPI_i0m_o400m           Modified_Catchment_Area
## 10                        Convexity                        slope_ws11
##                              k 5
## 1        ChannelNetworkBaseLevel
## 2                Catchment_slope
## 3          slope_DTM_50m_avg_ws7
## 4     Channel_Network_Base_Level
## 5                        Texture
## 6             Closed_Depressions
## 7                  TPI_i0m_o500m
## 8      Topographic_Wetness_Index
## 9  sagaTopographic_Wetness_Index
## 10                     LS_Factor

##                           allchosen Freq
## 1                   Catchment_slope    5
## 2           ChannelNetworkBaseLevel    5
## 3        Channel_Network_Base_Level    5
## 16                          Texture    5
## 13            slope_DTM_50m_avg_ws7    4
## 5                         Convexity    3
## 8           Modified_Catchment_Area    3
## 17        Topographic_Wetness_Index    3
## 19                    TPI_i0m_o500m    3
## 20 VerticalDistancetoChannelNetwork    3
## 4                Closed_Depressions    2
## [1] "10fold cv-error:  0.378117048346056  for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Modified_Catchment_Area AND VerticalDistancetoChannelNetwork AND Catchment_slope AND Topographic_Wetness_Index AND TPI_i0m_o400m AND Convexity"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   33  16   0   3   0   0   0   0   0   0   0  10   2   0   0   0   0
##   Ant   7  52   0   2   0   1   1   1   0   0   0   5   1   3   2   0   0
##   CBD   0   0  81   0   3   0   0   0   2   0   0   0   2   2  15   0   1
##   CD    6   4   0  41   0  12   4   0   0   2   3   0   5   1   1   0   9
##   CSR   0   0   0   0  84   0   0   0   0   0   0   0   0   0   6   0   0
##   DC    9   0   0   7   0  68   2   0   0   0   0   1   1   0   0   0   3
##   GLD  12  14   0  17   0   1 167  23   0   0  12   0  11   3   3   0   3
##   IMS   5   0   1   7   0   3  13  68   0   0   2   0   5   3   1   0   1
##   ISR   0   0   2   0   2   0   0   0  83   1   0   0  11   0  11   4  10
##   LD    2   1   2   3   0   1   7   0   0  67   3   0   1   5   7   0   8
##   LT    4   0   0   2   0   1   4   0   0   6  67   0   0   0   1   0  17
##   MrD  10   4   0   0   0   3   0   0   0   0   0  66   0   0   0   0   0
##   MxD   1   7   9   8   0   7   0   2   3   8   2   0  55   3   8   2   7
##   SB    1   0   0   1   0   0   0   0   0   1   2   1   0  61   8   0   5
##   SD    1   1   4   0   2   0   1   2   2   6   1   0   0   6  29   0   0
##   SSR   0   0   0   3   0   0   0   0   6   0   0   0   2   4   4  89   8
##   TG    0   0   2   7   0   3   0   1   4   9   8   0   4   9   4   5 129
##   WB    0   0   0   0   0   0   1   0   0   0   0   1   0   0   0   0   0
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    0
##   CSR   0
##   DC    0
##   GLD   4
##   IMS   1
##   ISR   0
##   LD    0
##   LT    0
##   MrD   0
##   MxD   2
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB   93
## [1] "Kappa overall =  0.657125344943974"
## [1] "Tau overall =  0.659452177817692"
## [1] "mean quality =  0.526415826968994"
## [1] "The quality is  0.526415826968994"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.686505135607319"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   59  29   0   1   0   1   0   0   0   0   0  16   2   0   1   0
##      Ant  15  83   0   4   0   0   3   1   0   2   0  10   1   2   4   0
##      CBD   0   0 146   0  13   0   0   0   7   1   0   0  13   1  32   0
##      CD   14  14   0  67   0  21  10   0   0   5  12   0  19   1   2   0
##      CSR   0   0   0   0 142   0   0   0   0   0   0   0   0   0  10   0
##      DC   20   0   0   9   0 120  10   1   0   3   1   4   0   0   0   0
##      GLD  20  43   0  48   0  18 304  58   0   0  26   3  19   7   2   0
##      IMS  10   0   3  13   0   4  35 116   0   0   4   0   5   6   6   0
##      ISR   0   0  11   2   8   0   0   0 130   0   0   0  28   2  30  20
##      LD    8   4  16   6   0   3  21   0   0 128  10   0   0  13  11   0
##      LT    6   6   0   5   0   0   8   0   0  12 120   0   6   7   2   0
##      MrD  28   2   0   1   0   5   0   0   0   0   0 143   1   0   0   0
##      MxD   1  11   5  24   0  21   0  10  20  12   8   0  83   2   8  21
##      SB    1   0   0   0   2   0   0   1   1   3   7   3   0 122  12   0
##      SD    0   5  11   0   6   1   1   7   2  13   0   0   7   9  61   0
##      SSR   0   0   0   3   3   0   0   0  29   0   0   0   7   6   3 150
##      TG    4   0   7  19   1   7   0   3  10  21  12   0   7  19   9   8
##      WB    0   0   0   0   0   0   9   0   0   0   0   3   0   1   0   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   0   0
##      CBD   6   0
##      CD   16   4
##      CSR   0   0
##      DC   10   0
##      GLD  13   9
##      IMS   6   0
##      ISR  33   0
##      LD   20   6
##      LT   27   1
##      MrD   0   3
##      MxD  15   2
##      SB   11   0
##      SD    0   1
##      SSR  10   0
##      TG  232   0
##      WB    0 175
## [1] "classification error rate with altdata:  0.393839103869654"
## [1] "Kappa overall =  0.580010208394313"
## [1] "Tau overall =  0.582993890020367"
## [1] "mean quality =  0.450640220514493"
## [1] "The quality is  0.450640220514493"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.622655565891132"

SVM n=200

##  [1] "Prediction error at end is:  0.739019209216254"
##  [2] "Prediction error at end is:  0.64581769489405" 
##  [3] "Prediction error at end is:  0.555157426955456"
##  [4] "Prediction error at end is:  0.501533544452264"
##  [5] "Prediction error at end is:  0.451994552610316"
##  [6] "Prediction error at end is:  0.423391195558683"
##  [7] "Prediction error at end is:  0.397850692000938"
##  [8] "Prediction error at end is:  0.374103396147731"
##  [9] "Prediction error at end is:  0.365163421690515"
## [10] "Prediction error at end is:  0.358779354653739"
##                                 k 1                              k 2
## 1           ChannelNetworkBaseLevel          ChannelNetworkBaseLevel
## 2                           Texture                          Texture
## 3             slope_DTM_50m_avg_ws7            slope_DTM_50m_avg_ws7
## 4        Channel_Network_Base_Level                    TPI_i0m_o400m
## 5                     TPI_i0m_o500m               Closed_Depressions
## 6                Closed_Depressions                        Convexity
## 7                         Convexity       Channel_Network_Base_Level
## 8  VerticalDistancetoChannelNetwork VerticalDistancetoChannelNetwork
## 9         Topographic_Wetness_Index                  Catchment_slope
## 10                  Catchment_slope                    slope_ws19_hr
##                                 k 3                              k 4
## 1           ChannelNetworkBaseLevel          ChannelNetworkBaseLevel
## 2                   Catchment_slope                          Texture
## 3             slope_DTM_50m_avg_ws5            slope_DTM_50m_avg_ws7
## 4                           Texture               Closed_Depressions
## 5                Closed_Depressions                    TPI_i0m_o500m
## 6        Channel_Network_Base_Level       Channel_Network_Base_Level
## 7  VerticalDistancetoChannelNetwork                  Catchment_slope
## 8                         Convexity                        Convexity
## 9                     TPI_i0m_o500m VerticalDistancetoChannelNetwork
## 10                        slope_ws5        DiurnalAnisotropicHeating
##                                 k 5
## 1           ChannelNetworkBaseLevel
## 2                           Texture
## 3             slope_DTM_50m_avg_ws7
## 4        Channel_Network_Base_Level
## 5                     TPI_i0m_o500m
## 6                Closed_Depressions
## 7                   Catchment_slope
## 8                         Convexity
## 9  VerticalDistancetoChannelNetwork
## 10                    slope_ws29_hr

##                           allchosen Freq
## 1                   Catchment_slope    5
## 2           ChannelNetworkBaseLevel    5
## 3        Channel_Network_Base_Level    5
## 4                Closed_Depressions    5
## 5                         Convexity    5
## 12                          Texture    5
## 16 VerticalDistancetoChannelNetwork    5
## 8             slope_DTM_50m_avg_ws7    4
## 15                    TPI_i0m_o500m    4
## [1] "10fold cv-error:  0.358452138492872  for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Modified_Catchment_Area AND VerticalDistancetoChannelNetwork AND Catchment_slope AND Topographic_Wetness_Index AND TPI_i0m_o400m AND Convexity"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   96  31   0   1   0   1   0   0   0   0   1  14   0   0   1   0   0
##   Ant   2  84   0   1   0   4   4   0   0   6   3   3   3   4   6   0   0
##   CBD   0   0 161   0   8   1   0   0   6   1   0   0  12   3  26   0   5
##   CD   18  15   0  87   0  11   9   3   0   3   3   2  27   2   2   0   9
##   CSR   0   0   1   0 146   0   0   0   0   0   0   0   0   0  10   0   0
##   DC   11   3   1  15   0 144   7   1   0   7   3   3   0   0   0   0  15
##   GLD  15  38   0  51   0  15 352  39   0   1  22   1  16   6   2   0  12
##   IMS   8   1   2  10   0   4  10 142   0   0   3   0   7   2   5   0   2
##   ISR   0   0   8   2   8   0   0   0 150   0   1   0  19   3  21   7  36
##   LD    2   4   7   3   0   2   4   0   0 148  11   0   1  15  17   0  21
##   LT    8   7   0   6   0   0   2   0   0   4 133   0   2  11   4   0  29
##   MrD  23   8   0   0   0   4   0   0   0   0   0 157   2   0   0   0   0
##   MxD   1   5   5   5   0   9   0   0  13   5   5   0  85   1   7   4   7
##   SB    1   0   1   0   0   1   0   2   0   4   4   0   0 127  10   2   5
##   SD    0   0   7   0  10   0   0   0   6   0   0   0   7   2  68   0   1
##   SSR   0   0   0   2   3   0   0   0  14   0   0   0   7   3   5 177  12
##   TG    1   1   6  19   0   5   2  10  10  21  11   0  10  18   9   9 246
##   WB    0   0   0   0   0   0  11   0   0   0   0   2   0   1   0   0   0
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    0
##   CSR   0
##   DC    2
##   GLD   4
##   IMS   0
##   ISR   0
##   LD    2
##   LT    2
##   MrD   2
##   MxD   1
##   SB    0
##   SD    1
##   SSR   0
##   TG    1
##   WB  186
## [1] "Kappa overall =  0.663525690742074"
## [1] "Tau overall =  0.666017730921289"
## [1] "mean quality =  0.530333884016433"
## [1] "The quality is  0.530333884016433"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.689100945495232"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   39  23   0   0   0   6   0   0   0   0   0  14   0   1   0   0
##      Ant   8  43   0   5   0   0   2   1   0   6   0   1   1   3   3   0
##      CBD   0   0  78   0   2   0   0   0   3   5   0   0   2   1  13   0
##      CD    6   5   0  40   0   7   9   1   0   1   2   0  11   3   0   0
##      CSR   0   0   1   0  78   0   0   0   0   0   0   0   0   0   4   0
##      DC    5   1   0  10   0  73   4   0   0   4   0   1   3   0   0   0
##      GLD  10  13   0  19   0   2 167  24   0   1  12   0   8   1   0   0
##      IMS   4   0   3   7   0   2   7  61   0   0   2   0   9   2   3   0
##      ISR   0   0   4   1   1   1   0   0  80   1   0   0  13   1  14   9
##      LD    3   1   3   3   0   1   1   0   0  67   5   0   1   7  13   0
##      LT    3   0   0   3   0   1   4   0   0   2  65   0   0   3   2   0
##      MrD  11   8   0   0   0   5   0   0   0   0   0  66   0   0   0   0
##      MxD   0   5   5   2   0   1   0   1   5   3   0   0  37   2   6   1
##      SB    1   0   0   1   0   0   0   1   0   2   6   0   0  58   9   0
##      SD    0   0   5   0  10   0   1   0   3   0   1   0   3   2  27   0
##      SSR   0   0   0   1   0   0   0   0   4   0   0   0   2   4   3  81
##      TG    1   0   2   9   0   1   1   8   5   8   7   0  10  12   3   9
##      WB    0   0   0   0   0   0   4   0   0   0   0   2   0   0   0   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   0   0
##      CBD   2   0
##      CD    6   1
##      CSR   0   0
##      DC   10   0
##      GLD   3   3
##      IMS   0   1
##      ISR  11   0
##      LD   11   0
##      LT   13   1
##      MrD   0   1
##      MxD   5   1
##      SB    7   0
##      SD    0   0
##      SSR   9   0
##      TG  123   0
##      WB    0  92
## [1] "classification error rate with altdata:  0.351145038167939"
## [1] "Kappa overall =  0.625607109612883"
## [1] "Tau overall =  0.628199371351594"
## [1] "mean quality =  0.489488966354227"
## [1] "The quality is  0.489488966354227"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.656597715709897"

RandomForest n=100

importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=1,altdata=twohundred,legend=geolegendeng)
## Loading required package: randomForest
## randomForest 4.6-10
## Type rfNews() to see new features/changes/bug fixes.
## [1] "OBB error with all predictors of  allterraincols is  0.368389780154486"
##                            MeanDecreaseGini                 parameters
## ChannelNetworkBaseLevel            59.40663    ChannelNetworkBaseLevel
## Channel_Network_Base_Level         49.80239 Channel_Network_Base_Level
## Catchment_slope                    30.71719            Catchment_slope
## Texture                            30.64480                    Texture
## slope_DTM_50m_avg_ws7              27.62740      slope_DTM_50m_avg_ws7
## Modified_Catchment_Area            27.47028    Modified_Catchment_Area
## Protection_Index                   21.42085           Protection_Index
## slope_DTM_50m_avg_ws5              19.62595      slope_DTM_50m_avg_ws5
## Closed_Depressions                 19.51936         Closed_Depressions
## TPI_i0m_o500m                      19.10673              TPI_i0m_o500m
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   50  12   0   1   0   1   2   0   0   0   1   3   0   0   0   0
##      Ant  14 109   0   1   0   2   6   2   0   0   0   7   2   1   4   0
##      CBD   0   0  45   0   2   0   0   3   7   4   0   0   7   0  11   3
##      CD   16   9   0  82   0  22  11   3   0   7  13   1  13   0   3   0
##      CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      DC   22   1   0  16   0 128   7   0   0   1   1   5   3   0   0   0
##      GLD  23  23   0  37   0  10 323  38   0   2  41   2  20   9   6   1
##      IMS   7   5   0   8   0   1   9 120   0   0   3   0   0   1   3   0
##      ISR   0   0  26   1   6   0   0   0 108   0   3   0  17   4  28  18
##      LD    4   4  13   5   0   4   1   0   0 146   8   0   4   9   7   0
##      LT    4   4   0   3   0   1   7   0   0   1  96   0   1  15   1   1
##      MrD  29   6   0   0   0   1   3   0   0   0   0 154   0   0   0   0
##      MxD   0   4   7   9   0  11   0   3   9   4   7   0  80   4   6  14
##      SB    4   6   7   0   7   0   4   5   3   2   6   1   0 115   9   0
##      SD    1   0  14   1   4   0   2   1  11  13   0   0   2  10  31   0
##      SSR   0   0   0   6   3   1   0   0  24   2   0   0   5   8   4 133
##      TG    0   1  10  17   0   5   1   8   8   5   8   0   9  12  11  13
##      WB    0   1   0   0   0   0   1   0   0   0   0   0   0   1   0   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   0   1
##      CBD   3   0
##      CD    7   4
##      CSR   0   0
##      DC   11   0
##      GLD  14   6
##      IMS   2   1
##      ISR  30   0
##      LD   16   0
##      LT   30   1
##      MrD   0   3
##      MxD  13   0
##      SB    7   0
##      SD    1   0
##      SSR  10   0
##      TG  228   0
##      WB    0 173
## [1] "classification error rate with altdata:  0.371555555555556"

##  [1] "Prediction error at end is:  0.669565612504544"
##  [2] "Prediction error at end is:  0.522186737290336"
##  [3] "Prediction error at end is:  0.407441449862388"
##  [4] "Prediction error at end is:  0.343193384223919"
##  [5] "Prediction error at end is:  0.292197642415745"
##  [6] "Prediction error at end is:  0.277930103339046"
##  [7] "Prediction error at end is:  0.2636586695747"  
##  [8] "Prediction error at end is:  0.265186685361167"
##  [9] "Prediction error at end is:  0.265691696525939"
## [10] "Prediction error at end is:  0.266708210001558"
##                                 k 1                              k 2
## 1        Channel_Network_Base_Level       Channel_Network_Base_Level
## 2             slope_DTM_50m_avg_ws7          ChannelNetworkBaseLevel
## 3           ChannelNetworkBaseLevel            slope_DTM_50m_avg_ws7
## 4                           Texture                          Texture
## 5  VerticalDistancetoChannelNetwork VerticalDistancetoChannelNetwork
## 6                   Catchment_Area2                  Catchment_slope
## 7                         Convexity                    TPI_i0m_o300m
## 8                      maxic_ws5_hr                    profc_ws11_hr
## 9             profc_DTM_50m_avg_ws7                  Catchment_Area2
## 10                    slope_ws13_hr                    TPI_i0m_o500m
##                           k 3                              k 4
## 1  Channel_Network_Base_Level       Channel_Network_Base_Level
## 2       slope_DTM_50m_avg_ws7          ChannelNetworkBaseLevel
## 3     ChannelNetworkBaseLevel                          Texture
## 4               TPI_i0m_o500m VerticalDistancetoChannelNetwork
## 5                     Texture            slope_DTM_50m_avg_ws7
## 6             Catchment_slope                    TPI_i0m_o500m
## 7                   Convexity                  Catchment_slope
## 8                  longc_ws11                        Convexity
## 9               slope_ws11_hr                    longc_ws23_hr
## 10               TPI_i0m_o10m                    maxic_ws19_hr
##                                 k 5
## 1        Channel_Network_Base_Level
## 2           ChannelNetworkBaseLevel
## 3                   Catchment_slope
## 4  VerticalDistancetoChannelNetwork
## 5                           Texture
## 6                     TPI_i0m_o140m
## 7                     TPI_i0m_o500m
## 8                        slope_ws15
## 9             profc_DTM_50m_avg_ws5
## 10          CrossSectionalCurvature

##                           allchosen Freq
## 3           ChannelNetworkBaseLevel    5
## 4        Channel_Network_Base_Level    5
## 18                          Texture    5
## 2                   Catchment_slope    4
## 14            slope_DTM_50m_avg_ws7    4
## 22                    TPI_i0m_o500m    4
## 23 VerticalDistancetoChannelNetwork    4
## 5                         Convexity    3
## 1                   Catchment_Area2    2
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","slope_DTM_50m_avg_ws7","Texture","ChannelNetworkBaseLevel", "Catchment_Area2","VerticalDistancetoChannelNetwork","Convexity"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.251399491094148  for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Catchment_Area2 AND VerticalDistancetoChannelNetwork AND Convexity"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   89   8   0   3   0   6   2   1   0   0   4   6   4   0   1   0
##      Ant  11 144   0   4   0   5   4   4   0   0   2   9   0   7   3   0
##      CBD   0   0 158   1   9   0   0   0   6   2   1   0  12   2  25   0
##      CD   12  14   0 100   0  11   3   0   1   2   9   0  12   4   1   0
##      CSR   0   0   5   0 143   0   0   0   1   0   0   0   0   0  19   1
##      DC   16   0   0  10   0 128   9   1   0   2   2   2   1   0   0   0
##      GLD  13  11   0  30   0  19 358   7   0   0   9   1   7   4   1   0
##      IMS   4   4   1  10   0   2  14 182   0   0   2   0   0   4   0   0
##      ISR   0   0   1   1  12   0   0   0 146   0   2   0  15   3  20  17
##      LD    1   2   7   7   0   7   0   0   0 164   2   0   0   5   5   0
##      LT    6   2   0  11   0   0   4   0   0   0 128   0   3   6   0   1
##      MrD  23   0   0   0   0   2   0   0   0   0   0 153   0   0   0   0
##      MxD   2   1   9  10   0  15   0   1  20   3  10   0 118   6   8  10
##      SB    1   4   8   2   1   2   1   1   2   4   8   2   0 129  25   0
##      SD    1   6   6   2   9   0   0   0   0  11   0   0  11   9  77   0
##      SSR   0   0   0   0   0   1   0   0  20   1   0   0  10   4   1 169
##      TG    7   1   4   9   1   2   0   0   3  11  21   0   5  13   6   1
##      WB    0   0   0   2   0   1   6   0   0   0   0   9   0   2   1   0
##         
## altpreds  TG  WB
##      AD    0   1
##      Ant   0   0
##      CBD   4   0
##      CD   13   1
##      CSR   0   0
##      DC   12   1
##      GLD   6   3
##      IMS   5   0
##      ISR  22   0
##      LD   22   4
##      LT   11   4
##      MrD   0   1
##      MxD  21   0
##      SB   13   0
##      SD    6   0
##      SSR   9   0
##      TG  256   0
##      WB    0 186
## [1] "classification error rate with altdata:  0.280040733197556"
## [1] "Kappa overall =  0.701880811385382"
## [1] "Tau overall =  0.703486282496705"
## [1] "mean quality =  0.565979175376853"
## [1] "The quality is  0.565979175376853"
## [1] "#########  Cramer's V =  0.715324152797654"

RandomForest n=200

##  [1] "Prediction error at end is:  0.617982250371413"
##  [2] "Prediction error at end is:  0.482891873224386"
##  [3] "Prediction error at end is:  0.361083418041546"
##  [4] "Prediction error at end is:  0.28472678343368" 
##  [5] "Prediction error at end is:  0.255105298824511"
##  [6] "Prediction error at end is:  0.240549821461152"
##  [7] "Prediction error at end is:  0.230081189563948"
##  [8] "Prediction error at end is:  0.222933119607996"
##  [9] "Prediction error at end is:  0.219612231865926"
## [10] "Prediction error at end is:  0.219104308390023"
##                                 k 1                              k 2
## 1        Channel_Network_Base_Level       Channel_Network_Base_Level
## 2           ChannelNetworkBaseLevel          ChannelNetworkBaseLevel
## 3                           Texture                  Catchment_slope
## 4  VerticalDistancetoChannelNetwork VerticalDistancetoChannelNetwork
## 5                   Catchment_slope                          Texture
## 6                         Convexity                    TPI_i0m_o500m
## 7                     TPI_i0m_o400m                       slope_ws15
## 8                        slope_ws15                        Convexity
## 9            Longitudinal_Curvature                  Catchment_Area2
## 10          Modified_Catchment_Area          Modified_Catchment_Area
##                           k 3                              k 4
## 1  Channel_Network_Base_Level       Channel_Network_Base_Level
## 2     ChannelNetworkBaseLevel          ChannelNetworkBaseLevel
## 3       slope_DTM_50m_avg_ws7                          Texture
## 4                     Texture VerticalDistancetoChannelNetwork
## 5               TPI_i0m_o400m                  Catchment_slope
## 6                   Convexity                       slope_ws15
## 7                  slope_ws11                        Convexity
## 8             Catchment_slope                    TPI_i0m_o500m
## 9     Modified_Catchment_Area          Modified_Catchment_Area
## 10     Longitudinal_Curvature                    longc_ws19_hr
##                                 k 5
## 1        Channel_Network_Base_Level
## 2             slope_DTM_50m_avg_ws5
## 3           ChannelNetworkBaseLevel
## 4                           Texture
## 5                     TPI_i0m_o500m
## 6                         Convexity
## 7             RelativeSlopePosition
## 8                   Catchment_slope
## 9                Catchment_slope_hr
## 10 VerticalDistancetoChannelNetwork

##                           allchosen Freq
## 2                   Catchment_slope    5
## 4           ChannelNetworkBaseLevel    5
## 5        Channel_Network_Base_Level    5
## 6                         Convexity    5
## 15                          Texture    5
## 9           Modified_Catchment_Area    4
## 18 VerticalDistancetoChannelNetwork    4
## 14                       slope_ws15    3
## 17                    TPI_i0m_o500m    3
## 8            Longitudinal_Curvature    2
## 16                    TPI_i0m_o400m    2
predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","slope_DTM_50m_avg_ws7","Texture","ChannelNetworkBaseLevel", "Catchment_Area2","VerticalDistancetoChannelNetwork","Convexity"),altdata=onehundred,legend=geolegendeng)
## [1] "OOB-error:  0.213849287169043  for predictors Channel_Network_Base_Level AND slope_DTM_50m_avg_ws7 AND Texture AND ChannelNetworkBaseLevel AND Catchment_Area2 AND VerticalDistancetoChannelNetwork AND Convexity"
## [1] "Kappa overall =  0.999458070778881"
## [1] "Tau overall =  0.999460884149994"
## [1] "mean quality =  0.99916455354319"
## [1] "The quality is  0.99916455354319"
## [1] "#########  Cramer's V =  0.999558344451984"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   48   5   0   1   0   8   2   0   0   0   0   5   1   0   0   0
##      Ant   9  84   0   6   0   1   2   2   0   0   1   0   0   2   1   0
##      CBD   0   0  84   0   4   0   0   0   2   0   0   0   6   3   9   0
##      CD    5   1   0  54   0   5   2   1   1   1   2   0   2   0   2   0
##      CSR   0   0   6   0  81   0   0   0   1   0   0   0   0   0   8   0
##      DC    4   1   0   9   0  75   3   0   0   2   0   1   1   0   0   0
##      GLD   6   2   0  11   0   4 184   6   0   0   8   0   4   1   0   0
##      IMS   1   0   0   4   0   0   2  88   0   0   2   0   1   0   1   0
##      ISR   0   0   0   0   1   1   0   0  79   1   0   0   7   2   5   4
##      LD    1   0   0   1   0   1   0   0   0  90   2   0   1   1   8   0
##      LT    4   0   1   5   0   0   2   0   0   0  71   0   3   5   1   1
##      MrD   7   6   0   0   0   2   2   0   0   0   0  77   0   0   0   0
##      MxD   3   0   3   5   0   2   0   0   5   0   2   0  71   5   4   1
##      SB    1   0   1   1   0   0   0   0   1   2   4   0   0  61  13   1
##      SD    0   0   2   0   5   0   0   0   6   1   2   0   1   9  40   0
##      SSR   0   0   0   1   0   0   0   0   2   0   1   0   0   3   3  93
##      TG    2   0   4   3   0   1   1   0   3   3   5   0   2   8   5   0
##      WB    0   0   0   0   0   0   0   0   0   0   0   1   0   0   0   0
##         
## altpreds  TG  WB
##      AD    2   0
##      Ant   0   0
##      CBD   1   0
##      CD    3   0
##      CSR   0   0
##      DC    6   0
##      GLD   1   2
##      IMS   1   0
##      ISR  10   0
##      LD    4   0
##      LT    7   0
##      MrD   0   0
##      MxD   5   0
##      SB   10   1
##      SD    3   0
##      SSR   6   0
##      TG  142   1
##      WB    0  96
## [1] "classification error rate with altdata:  0.227480916030534"
## [1] "Kappa overall =  0.757820838345675"
## [1] "Tau overall =  0.759137853614728"
## [1] "mean quality =  0.634164152539504"
## [1] "The quality is  0.634164152539504"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.768004942541285"

LOCAL TERRAIN

SVM n=100

##  [1] "Prediction error at end is:  0.796222152983331"
##  [2] "Prediction error at end is:  0.736098561562029"
##  [3] "Prediction error at end is:  0.691769226774679"
##  [4] "Prediction error at end is:  0.661217998649842"
##  [5] "Prediction error at end is:  0.646958248948434"
##  [6] "Prediction error at end is:  0.64492132730955" 
##  [7] "Prediction error at end is:  0.647467154800852"
##  [8] "Prediction error at end is:  0.646461027158955"
##  [9] "Prediction error at end is:  0.645952121306538"
## [10] "Prediction error at end is:  0.641889962091707"
##                       k 1                    k 2                    k 3
## 1   slope_DTM_50m_avg_ws7                  Slope  slope_DTM_50m_avg_ws5
## 2               Convexity  longc_DTM_50m_avg_ws7              Convexity
## 3   profc_DTM_50m_avg_ws7  slope_DTM_50m_avg_ws7  profc_DTM_50m_avg_ws5
## 4   crosc_DTM_50m_avg_ws7              Convexity Longitudinal_Curvature
## 5           slope_ws29_hr  planc_DTM_50m_avg_ws7              slope_ws7
## 6   maxic_DTM_50m_avg_ws5  maxic_DTM_50m_avg_ws5           planc_ws7_hr
## 7          Plan_Curvature  crosc_DTM_50m_avg_ws7  maxic_DTM_50m_avg_ws7
## 8            planc_ws7_hr          slope_ws29_hr  slope_DTM_50m_avg_ws7
## 9       Convergence_Index          slope_ws23_hr  maxic_DTM_50m_avg_ws3
## 10 Longitudinal_Curvature Longitudinal_Curvature             crosc_ws15
##                       k 4                       k 5
## 1   slope_DTM_50m_avg_ws7     slope_DTM_50m_avg_ws7
## 2               Convexity                 Convexity
## 3   profc_DTM_50m_avg_ws7     profc_DTM_50m_avg_ws7
## 4   maxic_DTM_50m_avg_ws5     minic_DTM_50m_avg_ws7
## 5   minic_DTM_50m_avg_ws7                 slope_ws7
## 6            slope_ws7_hr     crosc_DTM_50m_avg_ws5
## 7   planc_DTM_50m_avg_ws5 DiurnalAnisotropicHeating
## 8              maxic_ws15         Minimal_Curvature
## 9  Longitudinal_Curvature         Convergence_Index
## 10          maxic_ws29_hr             crosc_ws19_hr

##                 allchosen Freq
## 2               Convexity    5
## 25  slope_DTM_50m_avg_ws7    5
## 9  Longitudinal_Curvature    4
## 11  maxic_DTM_50m_avg_ws5    3
## 22  profc_DTM_50m_avg_ws7    3
## 1       Convergence_Index    2
## 4   crosc_DTM_50m_avg_ws7    2
## 15  minic_DTM_50m_avg_ws7    2
## 20           planc_ws7_hr    2
## 27          slope_ws29_hr    2
## 28              slope_ws7    2
## [1] "10fold cv-error:  0.630091649694501  for predictors slope_DTM_50m_avg_ws7 AND Convexity AND profc_DTM_50m_avg_ws7 AND planc_DTM_50m_avg_ws7 AND maxic_DTM_50m_avg_ws5"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   28   2   0   4   0   4   0   0   0   2   6   1   3   0   0   3   4
##   Ant  12  45   0   5   0   7   8   0   1   8   2   5   3   0   7   0   1
##   CBD   0   0 134   0  20   0   0   2  26  10   0   0  21   7  62  25   9
##   CD    7   2   0  30   0   3   9   0   2   1   8   3   7   5   4   9   4
##   CSR   0   0   5   0 121   0   0   0   1   2   0   0   0  18   7   1   0
##   DC   22   4   0  30   0  75  13   0   0  13   9   1   3   0   1   2  12
##   GLD  25  78   0  64   1  68 279  51   2 111  62   4  72  28  11  34  63
##   IMS  10   8   0  16   0   5  36 107   7   2   6   0   9   6   6   6  14
##   ISR   0   0  29   0   6   0   0   7  92   3   1   0  22   6  26  23  26
##   LD    0   2   3   0   2   0   0   0   0  12   0   0   0   2   2   0   0
##   LT    0   6   0  10   0   5  38   0   0   7  82   0   5  20   2   8  29
##   MrD  16  13   0   2   0  14   0   0   0   0   0 103   0   0   0   0   0
##   MxD   1   4   1   5   0   6   4   2  11   2   6   0  29   1   6   2   5
##   SB    4   9   1   3  10   1   2   4   3   0   6   0   0  85   6   6   3
##   SD    0   0   6   1   0   1   1   6   4   9   0   0   5   5  26   5   0
##   SSR   0   0   9   6   8   0   0   0  22   2   0   0   4   5  14  52   8
##   TG    0   3  11  15   7   2   3  18  28  16   8   0  15   9  13  23 222
##   WB   61  21   0  11   0  10   8   0   0   0   4  65   0   1   0   0   0
##      
## preds  WB
##   AD    6
##   Ant   8
##   CBD   0
##   CD    6
##   CSR   0
##   DC    3
##   GLD  13
##   IMS   3
##   ISR   0
##   LD    0
##   LT    2
##   MrD  42
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB  118
## [1] "Kappa overall =  0.375059566470827"
## [1] "Tau overall =  0.383251467593147"
## [1] "mean quality =  0.252753790022417"
## [1] "The quality is  0.252753790022417"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.435516352305733"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    8   1   0   3   0   3   1   0   0   0   3   1   1   1   0   1
##      Ant  10  24   0   5   0   4   3   0   0   2   2   3   1   4   5   0
##      CBD   0   0  61   0  13   0   0   1  12  10   1   0   4   5  34  15
##      CD    2   0   0   7   0   7   3   0   0   1   3   1   3   1   1   3
##      CSR   0   0   3   0  66   0   0   0   1   2   0   0   0  14   4   0
##      DC   11   3   0  14   0  41   7   0   0   6   4   0   4   1   0   1
##      GLD  18  29   0  33   0  27 143  19   0  57  39   2  42  17   4  20
##      IMS   8   6   3   8   0   3  13  56   0   2   0   0   4   3   5   7
##      ISR   0   0  15   1   1   0   1   2  45   0   2   0  14   2  16   9
##      LD    0   1   0   0   1   0   0   0   0   9   0   0   1   1   1   0
##      LT    2   0   0  10   1   3  17   0   0   2  35   0   0   7   5   5
##      MrD   4  10   0   1   0   8   0   0   0   0   0  46   0   0   0   0
##      MxD   2   1   1   3   0   0   4   2   6   1   0   0  14   1   1   1
##      SB    1   9   0   3   5   0   0   2   2   0   8   0   0  35   2   4
##      SD    0   0   1   0   1   0   0   2   2   1   0   0   0   1  10   1
##      SSR   1   0   6   2   3   0   0   0  12   0   0   0   3   4   3  25
##      TG    1   1  11   3   0   1   4  13  20   7   3   0   9   3   9   8
##      WB   23  14   0   8   0   3   4   0   0   0   0  31   0   0   0   0
##         
## altpreds  TG  WB
##      AD    2   1
##      Ant   0   5
##      CBD   5   0
##      CD    4   2
##      CSR   0   0
##      DC    7   0
##      GLD  30   4
##      IMS   8   0
##      ISR  12   0
##      LD    0   0
##      LT   11   1
##      MrD   0  17
##      MxD   2   0
##      SB   11   2
##      SD    0   0
##      SSR   4   0
##      TG  105   0
##      WB    0  68
## [1] "classification error rate with altdata:  0.593893129770992"
## [1] "Kappa overall =  0.362834447626198"
## [1] "Tau overall =  0.371171980242479"
## [1] "mean quality =  0.239253882024838"
## [1] "The quality is  0.239253882024838"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.425124668256516"

SVM n=200

##  [1] "Prediction error at end is:  0.788630248277655"
##  [2] "Prediction error at end is:  0.71928018978292" 
##  [3] "Prediction error at end is:  0.665479331860133"
##  [4] "Prediction error at end is:  0.64941732744053" 
##  [5] "Prediction error at end is:  0.622390809827116"
##  [6] "Prediction error at end is:  0.621111724944755"
##  [7] "Prediction error at end is:  0.609894384505394"
##  [8] "Prediction error at end is:  0.605299298063174"
##  [9] "Prediction error at end is:  0.599186598206161"
## [10] "Prediction error at end is:  0.595619394254517"
##                          k 1                       k 2
## 1      slope_DTM_50m_avg_ws7     slope_DTM_50m_avg_ws7
## 2                  Convexity                 Convexity
## 3     Longitudinal_Curvature    Longitudinal_Curvature
## 4                 slope_ws11     crosc_DTM_50m_avg_ws7
## 5      planc_DTM_50m_avg_ws7                slope_ws11
## 6      minic_DTM_50m_avg_ws7     maxic_DTM_50m_avg_ws7
## 7                 maxic_ws15     profc_DTM_50m_avg_ws7
## 8      maxic_DTM_50m_avg_ws7             profc_ws29_hr
## 9  DiurnalAnisotropicHeating DiurnalAnisotropicHeating
## 10         Convergence_Index             slope_ws11_hr
##                          k 3                       k 4
## 1      slope_DTM_50m_avg_ws7     slope_DTM_50m_avg_ws7
## 2                  Convexity                 Convexity
## 3     Longitudinal_Curvature     minic_DTM_50m_avg_ws7
## 4                 slope_ws11     profc_DTM_50m_avg_ws7
## 5      minic_DTM_50m_avg_ws7             slope_ws23_hr
## 6          Convergence_Index     maxic_DTM_50m_avg_ws7
## 7  DiurnalAnisotropicHeating DiurnalAnisotropicHeating
## 8      maxic_DTM_50m_avg_ws7         Convergence_Index
## 9      profc_DTM_50m_avg_ws7              planc_ws5_hr
## 10             slope_ws13_hr     profc_DTM_50m_avg_ws5
##                          k 5
## 1      slope_DTM_50m_avg_ws7
## 2                  Convexity
## 3     Longitudinal_Curvature
## 4      crosc_DTM_50m_avg_ws7
## 5                  slope_ws7
## 6      maxic_DTM_50m_avg_ws7
## 7      profc_DTM_50m_avg_ws7
## 8  DiurnalAnisotropicHeating
## 9          Convergence_Index
## 10     planc_DTM_50m_avg_ws7

##                    allchosen Freq
## 2                  Convexity    5
## 4  DiurnalAnisotropicHeating    5
## 6      maxic_DTM_50m_avg_ws7    5
## 14     slope_DTM_50m_avg_ws7    5
## 1          Convergence_Index    4
## 5     Longitudinal_Curvature    4
## 12     profc_DTM_50m_avg_ws7    4
## 8      minic_DTM_50m_avg_ws7    3
## 15                slope_ws11    3
## 3      crosc_DTM_50m_avg_ws7    2
## 9      planc_DTM_50m_avg_ws7    2
## [1] "10fold cv-error:  0.635114503816794  for predictors slope_DTM_50m_avg_ws7 AND Convexity AND Longitudinal_Curvature AND slope_ws11 AND crosc_DTM_50m_avg_ws7"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   15  11   0   2   0   1   3   0   0   0   3   4   0   0   0   0   1
##   Ant   3  10   0   2   0   1   2   2   0   0   0   1   0   3   1   1   1
##   CBD   0   0  50   0   6   0   1   1   4   2   1   0   1   1  17   1   1
##   CD    0   0   0   8   0   3   0   0   0   0   1   0   0   2   0   0   1
##   CSR   0   0   2   0  66   0   0   0   0   3   0   0   0   5   4   1   0
##   DC   12   7   0  21   0  39   4   1   0  10   3   1   4   0   0   3   6
##   GLD  18  31   0  31   0  35 138  23   0  56  41   0  38  15   1  17  32
##   IMS   5   9   0   9   0   2  17  42   0   0   0   0   5   2   4   3   4
##   ISR   0   0  24   0   5   0   1   5  64   0   2   0  12   7  19  19  14
##   LD    0   1   5   0   0   0   0   0   0  15   1   0   1   0   2   0   0
##   LT    2   0   0  10   1   3  16   0   0   2  41   1   0   6   5   4  16
##   MrD   3   6   0   0   0   6   0   0   0   0   0  35   0   0   0   0   0
##   MxD   1   0   1   4   0   1   6   6   5   0   0   0  18   2   1   0   2
##   SB    1   9   0   0   5   0   2   0   1   0   1   0   0  48   3   4   3
##   SD    2   1   5   0   5   0   0   1   2   4   0   0   1   4  23   0   3
##   SSR   0   0   5   0   2   0   0   2   8   0   2   0   2   1   7  38   3
##   TG    3   2   9   6   1   2   3  14  16   8   3   0  18   4  13   9 114
##   WB   26  12   0   8   0   7   7   0   0   0   1  42   0   0   0   0   0
##      
## preds  WB
##   AD    5
##   Ant   3
##   CBD   0
##   CD    3
##   CSR   0
##   DC    1
##   GLD   1
##   IMS   0
##   ISR   0
##   LD    0
##   LT    0
##   MrD   9
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB   78
## [1] "Kappa overall =  0.386451408409586"
## [1] "Tau overall =  0.394881005837449"
## [1] "mean quality =  0.264477222683818"
## [1] "The quality is  0.264477222683818"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.45414061945107"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   24   8   0   5   0   2   2   0   0   1   6   3   0   0   0   2
##      Ant   2  17   0   5   0   4   6   3   0   3   1   2   2   1   1   1
##      CBD   0   0  82   0  16   0   0   4  17   3   0   0  10   2  38   7
##      CD    4   3   0  16   0   1   2   0   0   1   2   2   8   3   1   0
##      CSR   1   2   7   1 109   0   0   0   0   3   0   0   1  17  12   0
##      DC   26   9   0  44   0  60  17   2   0   7   4   0   7   1   1   6
##      GLD  35  51   0  58   2  85 244  48   0 119  76   0  68  29   9  34
##      IMS   9  15   0  13   0   7  45  72   3   5   3   0   5   2  12   7
##      ISR   0   0  44   1  18   0   0   9  83   5   0   0  22  13  46  38
##      LD    0   1  13   0   0   0   0   0   0   9   0   0   0   0   7   0
##      LT    3   8   0  10   0   6  39   1   0   7  85   1   0  21   4   8
##      MrD   9  11   0   1   0   8   0   0   0   0   0  76   0   0   0   0
##      MxD   0   2   2   8   0   6   3  11   8   1   5   0  24   3   1   5
##      SB    3  22   3   1  16   1   7   1   5   3   4   0   0  69   2   7
##      SD    1   6  17   0   7   1   1   5  10  19   0   0   5  10  34   6
##      SSR   0   0  12   2   3   1   0   4  31   1   3   0   6  12   8  55
##      TG    3  10  19  22   4   4  12  37  42  13  10   0  39  14  17  23
##      WB   66  32   0  15   0  15  23   0   0   0   1  98   1   1   0   0
##         
## altpreds  TG  WB
##      AD    1  26
##      Ant   2   2
##      CBD   9   0
##      CD    2   2
##      CSR   0   0
##      DC    9   6
##      GLD  72   6
##      IMS  13   3
##      ISR  26   0
##      LD    0   0
##      LT   27   2
##      MrD   0  31
##      MxD   6   0
##      SB    4   0
##      SD    6   0
##      SSR   6   0
##      TG  216   0
##      WB    1 123
## [1] "classification error rate with altdata:  0.644093686354379"
## [1] "Kappa overall =  0.308333696989463"
## [1] "Tau overall =  0.318018449742422"
## [1] "mean quality =  0.203829530138025"
## [1] "The quality is  0.203829530138025"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.391547891288425"

RandomForest n=100

importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=2,altdata=twohundred,legend=geolegendeng)
## [1] "OBB error with all predictors of  localterrain is  0.534740092640247"
##                       MeanDecreaseGini            parameters
## slope_DTM_50m_avg_ws7         46.29627 slope_DTM_50m_avg_ws7
## slope_DTM_50m_avg_ws5         37.96943 slope_DTM_50m_avg_ws5
## Convexity                     34.14706             Convexity
## slope_DTM_50m_avg_ws3         30.03132 slope_DTM_50m_avg_ws3
## slope_ws15                    29.38625            slope_ws15
## Slope                         26.60654                 Slope
## slope_ws29_hr                 25.83931         slope_ws29_hr
## slope_ws11                    25.51571            slope_ws11
## slope_ws23_hr                 24.41085         slope_ws23_hr
## slope_ws7                     24.33957             slope_ws7
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   34  18   0   0   0   2   2   0   0   0   1   2   0   0   0   0
##      Ant   8  63   0   3   2   2   1   0   0   2   2   6   0   2   3   0
##      CBD   2   2  85   2  17   0   1  11  17  16   1   0  10   2  34  28
##      CD   17  11   0  71   0  34  13   5   0   8  25   4  15   2   2   5
##      CSR   0   0   9   0 108   0   0   0   8   5   0   0   0  41   9   4
##      DC   22   4   0  29   0 104   8   0   0   7  12   4   5   0   0   1
##      GLD  27  38   2  43   2  26 293  37   4  67  75   2  53  36  10  34
##      IMS   8   8   2   9   1   4  17  98   1   5   2   0   1   4   6   3
##      ISR   2   0  37   0  12   0   5   4  82   3   4   0  16  10  31  39
##      LD    3   4   2   5   0   3   3   0   0  45   6   0   2   1   1   2
##      LT    4   9   0   7   0  10  19   0   0  11  48   0   1   7   1   2
##      MrD  44   8   0   3   0   2   4   0   0   0   0 148   0   0   0   0
##      MxD   2   3   6  11   1  10   6  13  13   1   6   0  59   2  12   9
##      SB    1  12   1   1  10   0   8   6   4   0   5   0   0  39   2   4
##      SD    1   0  34   1   8   0   3   2  31  19   1   0  18  12  63  16
##      SSR   1   1   4   0   8   0   2   3  14   1   1   0   3   8   5  41
##      TG    4  13  17  16   5   3  14  17  25  10  11   0  15  32  11  11
##      WB    2   2   0   0   0   0   1   0   0   0   0   9   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   2   2
##      CBD   9   0
##      CD   11   6
##      CSR   0   0
##      DC   10   0
##      GLD  62   7
##      IMS   6   1
##      ISR  21   0
##      LD    4   0
##      LT   21   0
##      MrD   0   7
##      MxD  10   0
##      SB    3   0
##      SD   10   0
##      SSR   1   0
##      TG  230   0
##      WB    0 166
## [1] "classification error rate with altdata:  0.543891170431211"

##  [1] "Prediction error at end is:  0.721872565820221"
##  [2] "Prediction error at end is:  0.642898686192034"
##  [3] "Prediction error at end is:  0.582794568208963"
##  [4] "Prediction error at end is:  0.545610687022901"
##  [5] "Prediction error at end is:  0.532864672586592"
##  [6] "Prediction error at end is:  0.500769849924703"
##  [7] "Prediction error at end is:  0.524709196655762"
##  [8] "Prediction error at end is:  0.501287843381627"
##  [9] "Prediction error at end is:  0.494148880926416"
## [10] "Prediction error at end is:  0.487535701303422"
##                        k 1                    k 2
## 1    slope_DTM_50m_avg_ws7                  Slope
## 2    profc_DTM_50m_avg_ws7  slope_DTM_50m_avg_ws5
## 3                Convexity              Convexity
## 4            slope_ws29_hr Longitudinal_Curvature
## 5    profc_DTM_50m_avg_ws3  longc_DTM_50m_avg_ws3
## 6               minic_ws15  crosc_DTM_50m_avg_ws5
## 7                    Slope          slope_ws23_hr
## 8  CrossSectionalCurvature  longc_DTM_50m_avg_ws7
## 9        Convergence_Index  crosc_DTM_50m_avg_ws7
## 10   longc_DTM_50m_avg_ws5  slope_DTM_50m_avg_ws7
##                          k 3                    k 4                   k 5
## 1      slope_DTM_50m_avg_ws7  slope_DTM_50m_avg_ws7 slope_DTM_50m_avg_ws3
## 2      profc_DTM_50m_avg_ws7  profc_DTM_50m_avg_ws7 longc_DTM_50m_avg_ws7
## 3                  Convexity              Convexity slope_DTM_50m_avg_ws7
## 4                      Slope                  Slope             Convexity
## 5            Total_Curvature  crosc_DTM_50m_avg_ws7       Total_Curvature
## 6      crosc_DTM_50m_avg_ws7        Total_Curvature minic_DTM_50m_avg_ws5
## 7                 planc_ws15           crosc_ws9_hr maxic_DTM_50m_avg_ws7
## 8  DiurnalAnisotropicHeating             minic_ws11         slope_ws15_hr
## 9      planc_DTM_50m_avg_ws7  crosc_DTM_50m_avg_ws5 crosc_DTM_50m_avg_ws7
## 10    Longitudinal_Curvature Longitudinal_Curvature maxic_DTM_50m_avg_ws5

##                 allchosen Freq
## 2               Convexity    5
## 24  slope_DTM_50m_avg_ws7    5
## 4   crosc_DTM_50m_avg_ws7    4
## 21                  Slope    4
## 11 Longitudinal_Curvature    3
## 20  profc_DTM_50m_avg_ws7    3
## 28        Total_Curvature    3
## 3   crosc_DTM_50m_avg_ws5    2
## 10  longc_DTM_50m_avg_ws7    2
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("slope_DTM_50m_avg_ws7","profc_DTM_50m_avg_ws7","Convexity","Slope","Total_Curvature","crosc_DTM_50m_avg_ws7"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.460050890585242  for predictors slope_DTM_50m_avg_ws7 AND profc_DTM_50m_avg_ws7 AND Convexity AND Slope AND Total_Curvature AND crosc_DTM_50m_avg_ws7"
## [1] "Kappa overall =  0.998375003445709"
## [1] "Tau overall =  0.998383475527616"
## [1] "mean quality =  0.996889954235226"
## [1] "The quality is  0.996889954235226"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.998359823983639"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   54  21   0   5   0   0   4   1   0   1   0   9   3   0   2   1
##      Ant  13 102   0   6   1   7  12   1   0   3   6  10   3   7   4   1
##      CBD   0   1  98   1  13   0   1   8  15   5   0   0  19   5  39  14
##      CD   20   7   1  58   0  15  16   1   0   7  14   1  14   5   1   1
##      CSR   0   0  12   0 122   0   0   0   4   3   0   0   0  17   8   2
##      DC   21   1   0  20   0 115  11   0   1   7   4   1   5   2   0   1
##      GLD  16  18   0  36   2  21 280  22   1  27  44   2  29  25   9  23
##      IMS   8  10   1  12   0   2  15 140   5   0   4   0   5   5   5   2
##      ISR   0   0  23   0   8   1   0   1  91   2   3   0  12   7  23  26
##      LD    7   8   6  10   0   8  10   0   0  92  12   0  13   4   8  16
##      LT    3   8   0  16   0   7  17   2   0  12  78   0   6  17   4   3
##      MrD  29   2   0   2   0   0   1   0   0   0   0 123   0   0   0   0
##      MxD   2   4   7  15   1  18  10   8   9   2   9   0  63   4  10   8
##      SB    1  12   1   2  16   2   6   4   3   5  11   0   1  63   8   4
##      SD    2   0  32   2   5   0   1   2  18  20   3   0  11   7  48   3
##      SSR   1   0   9   9   5   0   5   4  28   1   2   0   9  14  10  86
##      TG    6   3   9   8   2   5  10   3  24  13  10   0   5  15  12   8
##      WB    3   0   0   0   0   0   2   0   0   0   0  36   0   1   2   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   7   2
##      CBD   9   0
##      CD    9   8
##      CSR   0   0
##      DC    7   2
##      GLD  28   6
##      IMS   9   1
##      ISR  20   0
##      LD   10   1
##      LT   32   2
##      MrD   0  13
##      MxD  15   0
##      SB    8   0
##      SD    4   0
##      SSR  12   0
##      TG  230   0
##      WB    0 166
## [1] "classification error rate with altdata:  0.488543788187373"
## [1] "Kappa overall =  0.4788607578659"
## [1] "Tau overall =  0.482718341919252"
## [1] "mean quality =  0.346595046049029"
## [1] "The quality is  0.346595046049029"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.517494591625497"

RandomForest n=200,

##  [1] "Prediction error at end is:  0.666749317561419"
##  [2] "Prediction error at end is:  0.600453334199922"
##  [3] "Prediction error at end is:  0.522687833095021"
##  [4] "Prediction error at end is:  0.48061484466398" 
##  [5] "Prediction error at end is:  0.463790458858703"
##  [6] "Prediction error at end is:  0.457163330300273"
##  [7] "Prediction error at end is:  0.439312686858183"
##  [8] "Prediction error at end is:  0.441353503184713"
##  [9] "Prediction error at end is:  0.446452944235019"
## [10] "Prediction error at end is:  0.439821591056805"
##                        k 1                     k 2
## 1    slope_DTM_50m_avg_ws7   slope_DTM_50m_avg_ws3
## 2    longc_DTM_50m_avg_ws7  Longitudinal_Curvature
## 3                Convexity               Convexity
## 4  CrossSectionalCurvature   slope_DTM_50m_avg_ws7
## 5               slope_ws11   planc_DTM_50m_avg_ws7
## 6    crosc_DTM_50m_avg_ws5   longc_DTM_50m_avg_ws3
## 7   Longitudinal_Curvature   profc_DTM_50m_avg_ws7
## 8                    Slope CrossSectionalCurvature
## 9          Total_Curvature   minic_DTM_50m_avg_ws5
## 10              profc_ws15           slope_ws23_hr
##                          k 3                       k 4
## 1      slope_DTM_50m_avg_ws7     slope_DTM_50m_avg_ws7
## 2      profc_DTM_50m_avg_ws7    Longitudinal_Curvature
## 3                  Convexity                 Convexity
## 4      crosc_DTM_50m_avg_ws7     planc_DTM_50m_avg_ws7
## 5                 slope_ws15     profc_DTM_50m_avg_ws7
## 6     Longitudinal_Curvature                 slope_ws7
## 7      longc_DTM_50m_avg_ws3     longc_DTM_50m_avg_ws3
## 8  DiurnalAnisotropicHeating           Total_Curvature
## 9                 minic_ws15     maxic_DTM_50m_avg_ws7
## 10           Total_Curvature DiurnalAnisotropicHeating
##                          k 5
## 1      slope_DTM_50m_avg_ws7
## 2      profc_DTM_50m_avg_ws7
## 3                  Convexity
## 4      planc_DTM_50m_avg_ws7
## 5      slope_DTM_50m_avg_ws3
## 6  DiurnalAnisotropicHeating
## 7      profc_DTM_50m_avg_ws3
## 8      planc_DTM_50m_avg_ws5
## 9      minic_DTM_50m_avg_ws5
## 10             longc_ws29_hr

##                    allchosen Freq
## 1                  Convexity    5
## 20     slope_DTM_50m_avg_ws7    5
## 9     Longitudinal_Curvature    4
## 16     profc_DTM_50m_avg_ws7    4
## 5  DiurnalAnisotropicHeating    3
## 6      longc_DTM_50m_avg_ws3    3
## 14     planc_DTM_50m_avg_ws7    3
## 25           Total_Curvature    3
## 4    CrossSectionalCurvature    2
## 11     minic_DTM_50m_avg_ws5    2
## 19     slope_DTM_50m_avg_ws3    2
predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("slope_DTM_50m_avg_ws7","longc_DTM_50m_avg_ws7","Convexity","crosc_DTM_50m_avg_ws7","slope_ws11"),altdata=onehundred,legend=geolegendeng)
## [1] "OOB-error:  0.428971486761711  for predictors slope_DTM_50m_avg_ws7 AND longc_DTM_50m_avg_ws7 AND Convexity AND crosc_DTM_50m_avg_ws7 AND slope_ws11"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   31   4   0   5   0   6   1   0   0   0   0  10   1   1   0   0
##      Ant   7  68   0   4   0   3   1   3   0   1   1   2   4   4   3   2
##      CBD   0   0  65   0   7   0   0   0   7   5   0   0   2   4  18   3
##      CD    5   0   1  34   0   8   3   2   1   1   5   0   4   1   0   4
##      CSR   0   0   4   0  67   0   0   0   1   2   0   0   0  11   6   1
##      DC    7   1   0  11   0  60   4   1   0   5   1   1   5   1   0   2
##      GLD  11   6   0  10   1   9 135   3   0   8  15   0  14  11   2   4
##      IMS   5   3   2   5   0   1   5  81   0   0   1   0   5   0   3   3
##      ISR   1   0   9   1   3   1   0   1  50   0   2   0  16   2  17   9
##      LD    2   2   0   7   0   5  10   0   2  57  12   0   5   3   1   4
##      LT    2   1   0   9   0   1  17   0   0   1  47   0   1  10   5   2
##      MrD  10   8   0   2   0   2   3   0   0   0   0  65   0   0   0   0
##      MxD   2   0   6   6   0   2   9   3   6   2   2   0  32   0   0   0
##      SB    1   2   0   1  10   1   6   0   3   1   5   0   0  33   5   3
##      SD    1   2   8   2   1   0   1   1  11  11   2   0   2   6  29   3
##      SSR   0   0   2   1   1   0   1   0  10   3   1   0   5   6   8  58
##      TG    2   1   4   3   1   1   4   2   9   3   6   0   4   7   3   2
##      WB    4   1   0   0   0   0   0   0   0   0   0   6   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   4
##      Ant   3   2
##      CBD   3   0
##      CD    6   2
##      CSR   0   0
##      DC    9   1
##      GLD  14   0
##      IMS   5   0
##      ISR  13   0
##      LD    9   0
##      LT    8   1
##      MrD   0  11
##      MxD   3   0
##      SB   12   1
##      SD    1   0
##      SSR   3   0
##      TG  112   0
##      WB    0  78
## [1] "classification error rate with altdata:  0.439185750636132"
## [1] "Kappa overall =  0.532471259772835"
## [1] "Tau overall =  0.534979793444095"
## [1] "mean quality =  0.394388336534084"
## [1] "The quality is  0.394388336534084"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.564347396552711"

REGIONAL TERRAIN

SVM n=100

##  [1] "Prediction error at end is:  0.78117048346056" 
##  [2] "Prediction error at end is:  0.702290076335878"
##  [3] "Prediction error at end is:  0.615776081424936"
##  [4] "Prediction error at end is:  0.590839694656489"
##  [5] "Prediction error at end is:  0.56030534351145" 
##  [6] "Prediction error at end is:  0.548600508905852"
##  [7] "Prediction error at end is:  0.550127226463104"
##  [8] "Prediction error at end is:  0.550127226463104"
##  [9] "Prediction error at end is:  0.553180661577608"
## [10] "Prediction error at end is:  0.550636132315522"
##                              k 1                           k 2
## 1     Channel_Network_Base_Level    Channel_Network_Base_Level
## 2                Catchment_slope sagaTopographic_Wetness_Index
## 3      Topographic_Wetness_Index               Catchment_slope
## 4        Modified_Catchment_Area     Topographic_Wetness_Index
## 5                      LS_Factor                     LS_Factor
## 6  sagaTopographic_Wetness_Index       Modified_Catchment_Area
## 7               MassBalanceIndex            Mass_Balance_Index
## 8                 Catchment_area                Catchment_area
## 9                            TWI                      LSFactor
## 10                      LSFactor                           TWI
##                              k 3                           k 4
## 1     Channel_Network_Base_Level    Channel_Network_Base_Level
## 2                Catchment_slope               Catchment_slope
## 3      Topographic_Wetness_Index     Topographic_Wetness_Index
## 4        Modified_Catchment_Area                     LS_Factor
## 5                      LS_Factor       Modified_Catchment_Area
## 6  sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## 7             Mass_Balance_Index                      LSFactor
## 8                       LSFactor                Catchment_area
## 9              Catchment_area_hr            Mass_Balance_Index
## 10         RelativeSlopePosition                           TWI
##                              k 5
## 1     Channel_Network_Base_Level
## 2                Catchment_slope
## 3      Topographic_Wetness_Index
## 4                      LS_Factor
## 5  sagaTopographic_Wetness_Index
## 6        Modified_Catchment_Area
## 7                       LSFactor
## 8             Mass_Balance_Index
## 9                 Catchment_area
## 10              MassBalanceIndex

##                        allchosen Freq
## 3                Catchment_slope    5
## 4     Channel_Network_Base_Level    5
## 5                       LSFactor    5
## 6                      LS_Factor    5
## 9        Modified_Catchment_Area    5
## 11 sagaTopographic_Wetness_Index    5
## 12     Topographic_Wetness_Index    5
## 1                 Catchment_area    4
## 8             Mass_Balance_Index    4
## 13                           TWI    3
## 7               MassBalanceIndex    2
## [1] "10fold cv-error:  0.54910941475827  for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND Modified_Catchment_Area AND LS_Factor AND sagaTopographic_Wetness_Index"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   31  13   0   2   0   0   0   0   0   0   0  18   0   0   0   0   0
##   Ant   3  22   0   2   0   5   3   6   0   0   0   3   0   1   0   0   0
##   CBD   0   0  58   0   9   0   0   2  22   2   1   0  12   4  20   0   4
##   CD    5   0   0  15   0   1   2   1   0   0   2   1   2   2   0   0   0
##   CSR   0   0   3   0  67   0   0   0   3   0   0   0   0   0   4   2   0
##   DC    9   5   0  24   0  69  10   0   0   7   1   2   5   0   1   0  12
##   GLD  23  36   0  27   0   9 141  54   0   1  21   1   8  16   3   8   5
##   IMS   1   2   0   1   0   0   1   0   0   0   0   0   1   3   0   5   0
##   ISR   0   0  17   0   5   0   0   0  46   1   0   0  11   1  20   8   9
##   LD    1   1   0   6   0   0   4   0   0  59  16   0   1  16   8  16  24
##   LT    2   2   0   5   0   1  27   6   0   0  49   0   1   8   1   3   8
##   MrD  11   8   0   1   0   4   2   0   0   0   0  58   0   0   0   0   0
##   MxD   1   4   2   3   0   3   0   0   4   0   0   0  27   1   1   0   5
##   SB    0   1   0   0   0   0   1   4   2   0   1   0   0  30   6   0   6
##   SD    0   0   2   0   0   0   0   0   1   0   0   0   0   0  10   0   0
##   SSR   0   0   0   4  10   0   0   0  16   0   0   0   1   1   4  47   4
##   TG    4   2  19  11   0   8   3  24   6  30   9   0  31  17  22  11 124
##   WB    0   3   0   0   0   0   6   0   0   0   0   1   0   0   0   0   0
##      
## preds  WB
##   AD    0
##   Ant   2
##   CBD   0
##   CD    1
##   CSR   0
##   DC    2
##   GLD   3
##   IMS   0
##   ISR   0
##   LD    1
##   LT    1
##   MrD   1
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB   89
## [1] "Kappa overall =  0.441499869140277"
## [1] "Tau overall =  0.448765154916929"
## [1] "mean quality =  0.315016139481741"
## [1] "The quality is  0.315016139481741"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.515444050043836"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   60  23   0   0   0   1   1   0   0   0   0  28   1   0   0   0
##      Ant  13  49   0   3   0   3   6  11   0   1   1   3   1   0   0   0
##      CBD   0   0 109   0  14   0   0   2  50   5   0   0  36   6  48   0
##      CD   16   4   0  24   0   1   3   0   0   2  10   3   7   1   2   0
##      CSR   0   0   3   0 115   0   0   0   8   0   0   0   0   1   7   8
##      DC   13  16   0  48   0 131  14   1   0   9   4   5   8   0   3   1
##      GLD  30  64   0  61   0  16 278 102   1   2  33   1  18  36   9  14
##      IMS   2   1   0   1   0   0  12   0   0   0   1   0   0   7   0   3
##      ISR   0   0  31   2  17   0   0   0  80   0   0   0  25   5  37  36
##      LD    5   5   3   9   1   5  18   0   0 121  44   0   1  24   5  21
##      LT    6   8   0   8   0   1  46  10   0   0  80   0   7  18   0   3
##      MrD  27   6   0   4   0   6   3   1   0   0   0 131   1   0   0   0
##      MxD   1   4   6   2   0  15   0   2  10   0   2   0  43   0   2  13
##      SB    3   4   3   1   3   0   4   7   2   4   6   0   0  57   9   0
##      SD    0   0   3   0   0   0   0   0   0   0   0   0   0   0  19   0
##      SSR   0   0   1   7  25   0   0   0  34   0   0   0  11   4   8  77
##      TG   10   8  40  32   0  22   4  61  14  56  19   0  39  38  44  23
##      WB    0   5   0   0   0   0  12   0   0   0   0  11   0   1   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD  17   0
##      CD    4   6
##      CSR   0   0
##      DC   16   3
##      GLD  16   8
##      IMS   2   0
##      ISR  26   0
##      LD   48   5
##      LT   13   0
##      MrD   0   3
##      MxD   2   0
##      SB    7   0
##      SD    0   0
##      SSR   4   0
##      TG  245   2
##      WB    0 174
## [1] "classification error rate with altdata:  0.543533604887984"
## [1] "Kappa overall =  0.416936409742774"
## [1] "Tau overall =  0.424493830118606"
## [1] "mean quality =  0.29301003758555"
## [1] "The quality is  0.29301003758555"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.492911276640849"

SVM n=200

##  [1] "Prediction error at end is:  0.756875901525097"
##  [2] "Prediction error at end is:  0.674394904458599"
##  [3] "Prediction error at end is:  0.596236365699097"
##  [4] "Prediction error at end is:  0.571034180969514"
##  [5] "Prediction error at end is:  0.544303333819549"
##  [6] "Prediction error at end is:  0.539974716779307"
##  [7] "Prediction error at end is:  0.518845399588337"
##  [8] "Prediction error at end is:  0.520370820570169"
##  [9] "Prediction error at end is:  0.52495518711204" 
## [10] "Prediction error at end is:  0.527245263447918"
##                              k 1                           k 2
## 1     Channel_Network_Base_Level    Channel_Network_Base_Level
## 2                Catchment_slope               Catchment_slope
## 3      Topographic_Wetness_Index     Topographic_Wetness_Index
## 4  sagaTopographic_Wetness_Index       Modified_Catchment_Area
## 5                      LS_Factor                     LS_Factor
## 6             Mass_Balance_Index            Mass_Balance_Index
## 7        Modified_Catchment_Area sagaTopographic_Wetness_Index
## 8                       LSFactor                           TWI
## 9                 Catchment_area                      LSFactor
## 10                           TWI                Catchment_area
##                              k 3                           k 4
## 1     Channel_Network_Base_Level    Channel_Network_Base_Level
## 2                Catchment_slope               Catchment_slope
## 3      Topographic_Wetness_Index     Topographic_Wetness_Index
## 4        Modified_Catchment_Area       Modified_Catchment_Area
## 5                      LS_Factor                     LS_Factor
## 6  sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## 7             Mass_Balance_Index            Mass_Balance_Index
## 8                            TWI                           TWI
## 9                       LSFactor               Catchment_Area2
## 10             Catchment_area_hr                      LSFactor
##                              k 5
## 1     Channel_Network_Base_Level
## 2                Catchment_slope
## 3      Topographic_Wetness_Index
## 4        Modified_Catchment_Area
## 5                      LS_Factor
## 6  sagaTopographic_Wetness_Index
## 7             Mass_Balance_Index
## 8                            TWI
## 9                       LSFactor
## 10               Catchment_Area2

##                        allchosen Freq
## 4                Catchment_slope    5
## 5     Channel_Network_Base_Level    5
## 6                       LSFactor    5
## 7                      LS_Factor    5
## 8             Mass_Balance_Index    5
## 9        Modified_Catchment_Area    5
## 10 sagaTopographic_Wetness_Index    5
## 11     Topographic_Wetness_Index    5
## 12                           TWI    5
## 1                 Catchment_area    2
## 2                Catchment_Area2    2
## [1] "10fold cv-error:  0.520366598778004  for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND Modified_Catchment_Area AND LS_Factor AND sagaTopographic_Wetness_Index"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   66  23   0   0   0   0   2   0   0   0   0  21   2   0   1   0   0
##   Ant   6  46   0   0   0   2   1   4   0   3   1   2   1   0   0   0   0
##   CBD   0   0  87   1  13   0   0   0  39   7   0   0  30   6  42   0  13
##   CD   18   5   0  33   0   3   3   1   0   2   8   2   6   0   2   0   5
##   CSR   0   0   3   0 118   0   0   0   8   0   0   0   1   1   7  10   0
##   DC   11  21   0  47   0 143  11   1   1  11   4   2  11   0   4   7  20
##   GLD  26  71   0  54   0  18 298  95   1   0  31   0  14  41   9  18  18
##   IMS   6   2   0   3   0   2   8  43   3   0   1   0   3   6   6   3   6
##   ISR   0   0  47   3  20   0   0   0  97   0   1   0  17   4  36  20  26
##   LD    8   4   4   8   0   4   5   2   0 131  49   0   1  29   7   5  49
##   LT    9  10   0  14   0   0  49  10   0   0  85   0   8  19   1   6  11
##   MrD  30  10   0   4   0   7   7   1   0   0   0 154   1   0   0   0   0
##   MxD   1   2   9   0   0   8   1   1  12   0   1   0  57   1   5  10   5
##   SB    1   1  12   1   2   0   3  10   2   3   5   0   0  73  15   0  13
##   SD    0   0   1   0   1   0   0   0   1   0   0   0   0   0  21   0   0
##   SSR   0   0   4  18  19   5   0   0  28   0   0   0  20   7   8 101  11
##   TG    4   1  32  16   2   9   2  29   7  43  14   0  26  10  29  19 223
##   WB    0   1   0   0   0   0  11   0   0   0   0   1   0   1   0   0   0
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    5
##   CSR   0
##   DC    4
##   GLD   4
##   IMS   1
##   ISR   0
##   LD    4
##   LT    0
##   MrD   4
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    1
##   WB  178
## [1] "Kappa overall =  0.462295688579045"
## [1] "Tau overall =  0.467892656044088"
## [1] "mean quality =  0.331750130769393"
## [1] "The quality is  0.331750130769393"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.523429876005305"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   29  13   0   3   0   1   0   0   0   0   0  19   0   0   0   0
##      Ant   1  22   0   0   0   4   0   1   0   1   0   2   0   0   2   0
##      CBD   0   0  48   0  10   0   0   1  19   2   1   0   7   2  18   0
##      CD    4   0   0  18   0   4   2   0   0   1   3   1   5   2   0   0
##      CSR   0   0   3   0  67   0   0   0   3   0   0   0   0   0   4   2
##      DC   10   9   0  24   0  70  10   0   1   8   2   2  10   0   1   1
##      GLD  23  39   0  29   0  10 136  50   0   1  23   0   6  15   3  12
##      IMS   2   1   0   2   0   0   4  15   1   0   2   0   4   6   1   2
##      ISR   0   0  23   0   5   0   0   0  48   1   1   0   9   2  17   8
##      LD    4   1   1   4   0   0   2   0   0  61  15   0   2  17   9   7
##      LT    1   1   0   5   0   1  33   9   0   0  47   0   2  11   1   6
##      MrD  15   9   0   1   0   4   5   0   0   0   0  59   0   0   0   0
##      MxD   1   2   5   2   0   3   1   1   5   0   0   0  26   2   2   0
##      SB    0   0   1   0   0   0   0   5   2   1   1   0   1  36  11   0
##      SD    0   0   1   0   1   0   0   0   1   0   0   0   0   0  13   0
##      SSR   0   0   3   8   8   1   0   0  15   0   0   0   8   2   5  48
##      TG    1   0  16   5   0   2   3  15   5  24   5   0  20   5  13  14
##      WB    0   2   0   0   0   0   4   0   0   0   0   1   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD   4   0
##      CD    0   0
##      CSR   0   0
##      DC   12   3
##      GLD   6   5
##      IMS   3   0
##      ISR  12   0
##      LD   25   1
##      LT    6   1
##      MrD   0   2
##      MxD   5   0
##      SB   13   0
##      SD    0   0
##      SSR   7   0
##      TG  108   0
##      WB    0  88
## [1] "classification error rate with altdata:  0.522137404580153"
## [1] "Kappa overall =  0.441203576026597"
## [1] "Tau overall =  0.447148630444544"
## [1] "mean quality =  0.317958141141582"
## [1] "The quality is  0.317958141141582"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.512285026855902"

RandomForest n=100

importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=3,altdata=twohundred,legend=geolegendeng)
## [1] "OBB error with all predictors of  regionalterrain is  0.363423212192263"
##                               MeanDecreaseGini
## Channel_Network_Base_Level           207.87310
## Modified_Catchment_Area              134.11583
## Catchment_slope                      124.26100
## Protection_Index                     120.13880
## LS_Factor                            109.43648
## sagaTopographic_Wetness_Index        104.55499
## RelativeSlopePosition                 89.92425
## Topographic_Wetness_Index             85.32774
## LSFactor                              84.57299
## Mass_Balance_Index                    78.86478
##                                                  parameters
## Channel_Network_Base_Level       Channel_Network_Base_Level
## Modified_Catchment_Area             Modified_Catchment_Area
## Catchment_slope                             Catchment_slope
## Protection_Index                           Protection_Index
## LS_Factor                                         LS_Factor
## sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
## RelativeSlopePosition                 RelativeSlopePosition
## Topographic_Wetness_Index         Topographic_Wetness_Index
## LSFactor                                           LSFactor
## Mass_Balance_Index                       Mass_Balance_Index
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   66  12   0   2   0   2   0   0   0   0   1   1   4   0   0   0
##      Ant  19 120   1   0   0   4   7   5   0   0   0  12   0   7   0   1
##      CBD   0   0  58   0   1   0   0   0  12   3   0   0  11   3   8   2
##      CD    8   7   0  64   0  12  11   0   0   0   7   0   5   0   1   1
##      CSR   0   0   1   0   1   0   0   0   2   0   0   0   0   0   0   0
##      DC   23   1   0  35   0 128   5   1   0   5   3   3   7   0   2   0
##      GLD  13  19   0  31   0  12 309  23   0   1  24   3  11  14   3   2
##      IMS   6   4   0   8   0   0  20 127   0   0   2   0   2  12   6   8
##      ISR   0   0  18   2  11   0   0   0 116   1   0   0  17   3  18  18
##      LD    2   3  10   5   0   3   1   0   0 149  13   0   0  17  10   0
##      LT    6   5   0  10   0   3   9   0   1   0  98   0   3  12   1   0
##      MrD  26   2   0   0   0   2   3   0   0   0   0 160   0   0   0   0
##      MxD   2   4   7   7   0  14   0   4   8   0   9   0  90   5   9  18
##      SB    3   6   9   0   3   1   8   6   3   7  20   0   0  90  15   0
##      SD    0   1   7   1   2   0   0   0   6   7   0   0   0  11  30   1
##      SSR   0   0   1   9   4   1   2   0  18   1   1   0   9  11   9 127
##      TG    5   1  10  12   0   6   1  18   4  12  11   0   4   3  14   5
##      WB    0   1   0   2   0   0   2   0   0   1   0   1   0   1   1   0
##         
## altpreds  TG  WB
##      AD    0   1
##      Ant   0   0
##      CBD   8   0
##      CD    8   0
##      CSR   0   0
##      DC   13   0
##      GLD  15   6
##      IMS   6   0
##      ISR  23   0
##      LD   17   0
##      LT   14   0
##      MrD   0   0
##      MxD  15   0
##      SB    9   0
##      SD    0   0
##      SSR   8   0
##      TG  237   3
##      WB    0 191
## [1] "classification error rate with altdata:  0.366089762393664"

##  [1] "Prediction error at end is:  0.660559796437659"
##  [2] "Prediction error at end is:  0.524681933842239"
##  [3] "Prediction error at end is:  0.437150127226463"
##  [4] "Prediction error at end is:  0.412213740458015"
##  [5] "Prediction error at end is:  0.385750636132316"
##  [6] "Prediction error at end is:  0.38676844783715" 
##  [7] "Prediction error at end is:  0.388295165394402"
##  [8] "Prediction error at end is:  0.391857506361323"
##  [9] "Prediction error at end is:  0.387786259541985"
## [10] "Prediction error at end is:  0.390330788804071"
##                              k 1                        k 2
## 1     Channel_Network_Base_Level Channel_Network_Base_Level
## 2                Catchment_slope            Catchment_slope
## 3        Modified_Catchment_Area  Topographic_Wetness_Index
## 4                      LS_Factor      RelativeSlopePosition
## 5      Topographic_Wetness_Index                  LS_Factor
## 6             Mass_Balance_Index    Modified_Catchment_Area
## 7          RelativeSlopePosition         Mass_Balance_Index
## 8  sagaTopographic_Wetness_Index          Catchment_area_hr
## 9                            TWI            Catchment_Area2
## 10                Catchment_area           MassBalanceIndex
##                              k 3                           k 4
## 1     Channel_Network_Base_Level    Channel_Network_Base_Level
## 2                Catchment_slope               Catchment_slope
## 3      Topographic_Wetness_Index       Modified_Catchment_Area
## 4          RelativeSlopePosition                     LS_Factor
## 5                      LS_Factor         RelativeSlopePosition
## 6        Modified_Catchment_Area     Topographic_Wetness_Index
## 7             Mass_Balance_Index                      LSFactor
## 8                       LSFactor              MassBalanceIndex
## 9               MassBalanceIndex               Catchment_Area2
## 10 sagaTopographic_Wetness_Index sagaTopographic_Wetness_Index
##                              k 5
## 1     Channel_Network_Base_Level
## 2                Catchment_slope
## 3        Modified_Catchment_Area
## 4                      LS_Factor
## 5          RelativeSlopePosition
## 6                            TWI
## 7              Catchment_area_hr
## 8             Mass_Balance_Index
## 9                Catchment_Area2
## 10 sagaTopographic_Wetness_Index

##                        allchosen Freq
## 4                Catchment_slope    5
## 5     Channel_Network_Base_Level    5
## 7                      LS_Factor    5
## 10       Modified_Catchment_Area    5
## 11         RelativeSlopePosition    5
## 9             Mass_Balance_Index    4
## 12 sagaTopographic_Wetness_Index    4
## 13     Topographic_Wetness_Index    4
## 2                Catchment_Area2    3
## 8               MassBalanceIndex    3
## 3              Catchment_area_hr    2
## 6                       LSFactor    2
## 14                           TWI    2
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","Catchment_slope","Topographic_Wetness_Index","RelativeSlopePosition","LS_Factor"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.361323155216285  for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND RelativeSlopePosition AND LS_Factor"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   70  13   0  11   0   1   8   2   0   0   5  12   4   0   1   0
##      Ant  13 134   0   4   0   7   3   2   0   2   3  13   1   8   1   0
##      CBD   1   0 127   1   7   0   0   1  13   1   0   0  18   7  24   1
##      CD   15  10   0  81   0  12  20   1   0   2  10   0   9   2   2   1
##      CSR   0   0   5   0 126   0   0   0  13   0   0   0   2   1   8   5
##      DC   13   5   0  31   0 123   4   1   0   2   6   3   7   0   1   3
##      GLD  17  17   0  23   0   9 314  23   0   0  19   5   8  17   4   4
##      IMS   1   1   1   9   0   0  17 149   0   0   3   0   2  14   6   3
##      ISR   0   0   9   0  22   2   0   0 129   0   0   0  16   6  37  17
##      LD    2   4   6   8   0   4   1   0   0 152  11   0   0  12   9   0
##      LT   11   5   0   2   0   2  11   0   0   4  95   0   2  17   3   1
##      MrD  33   2   0   0   0   3   2   0   0   0   0 148   0   0   0   0
##      MxD   2   2  10   5   0  15   1   3  16   3   3   0  98   2   8   9
##      SB    1   2  13   2   2   1   9   7   2   4  12   0   1  81  15   4
##      SD    0   1  13   2   6   0   0   3   6   9   3   0  10   9  59   0
##      SSR   0   1   0   6  12   3   3   1  13   1   1   0   9  10   6 142
##      TG    7   0  15  17   0  19   4   4   7  20  29   0  11  11   9   9
##      WB    0   0   0   0   0   0   4   0   0   0   0   1   0   1   0   0
##         
## altpreds  TG  WB
##      AD    2   2
##      Ant   1   1
##      CBD   4   0
##      CD   10   5
##      CSR   0   0
##      DC   13   3
##      GLD  10   9
##      IMS   3   0
##      ISR  28   0
##      LD   23   1
##      LT   11   1
##      MrD   0   1
##      MxD  21   0
##      SB   13   0
##      SD    3   0
##      SSR   3   0
##      TG  255   1
##      WB    0 177
## [1] "classification error rate with altdata:  0.373727087576375"
## [1] "Kappa overall =  0.601655549969939"
## [1] "Tau overall =  0.604288966095603"
## [1] "mean quality =  0.461701262064355"
## [1] "The quality is  0.461701262064355"
## [1] "#########  Cramer's V =  0.625758372970374"

RandomForest n=200

##  [1] "Prediction error at end is:  0.623474497982205"
##  [2] "Prediction error at end is:  0.482946143498485"
##  [3] "Prediction error at end is:  0.385441402894605"
##  [4] "Prediction error at end is:  0.35565549991086" 
##  [5] "Prediction error at end is:  0.328666958396136"
##  [6] "Prediction error at end is:  0.318741025267013"
##  [7] "Prediction error at end is:  0.322307904247905"
##  [8] "Prediction error at end is:  0.324597332296073"
##  [9] "Prediction error at end is:  0.325613199137777"
## [10] "Prediction error at end is:  0.324851785222282"
##                           k 1                           k 2
## 1  Channel_Network_Base_Level    Channel_Network_Base_Level
## 2             Catchment_slope               Catchment_slope
## 3   Topographic_Wetness_Index       Modified_Catchment_Area
## 4       RelativeSlopePosition         RelativeSlopePosition
## 5                   LS_Factor                     LS_Factor
## 6     Modified_Catchment_Area               Catchment_Area2
## 7          Mass_Balance_Index            Mass_Balance_Index
## 8            MassBalanceIndex                           TWI
## 9           Catchment_area_hr             Catchment_area_hr
## 10             Catchment_area sagaTopographic_Wetness_Index
##                              k 3                           k 4
## 1     Channel_Network_Base_Level    Channel_Network_Base_Level
## 2                Catchment_slope               Catchment_slope
## 3        Modified_Catchment_Area     Topographic_Wetness_Index
## 4          RelativeSlopePosition                     LS_Factor
## 5                      LS_Factor         RelativeSlopePosition
## 6             Mass_Balance_Index sagaTopographic_Wetness_Index
## 7                Catchment_Area2       Modified_Catchment_Area
## 8                       LSFactor              MassBalanceIndex
## 9                 Catchment_area            Mass_Balance_Index
## 10 sagaTopographic_Wetness_Index                      LSFactor
##                           k 5
## 1  Channel_Network_Base_Level
## 2             Catchment_slope
## 3     Modified_Catchment_Area
## 4                   LS_Factor
## 5       RelativeSlopePosition
## 6              Catchment_area
## 7          Mass_Balance_Index
## 8                    LSFactor
## 9   Topographic_Wetness_Index
## 10            Catchment_Area2

##                        allchosen Freq
## 4                Catchment_slope    5
## 5     Channel_Network_Base_Level    5
## 7                      LS_Factor    5
## 9             Mass_Balance_Index    5
## 10       Modified_Catchment_Area    5
## 11         RelativeSlopePosition    5
## 1                 Catchment_area    3
## 2                Catchment_Area2    3
## 6                       LSFactor    3
## 12 sagaTopographic_Wetness_Index    3
## 13     Topographic_Wetness_Index    3
## 3              Catchment_area_hr    2
## 8               MassBalanceIndex    2
predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("Channel_Network_Base_Level","Catchment_slope","Topographic_Wetness_Index","RelativeSlopePosition","LS_Factor"),altdata=onehundred,legend=geolegendeng)
## [1] "OOB-error:  0.307281059063136  for predictors Channel_Network_Base_Level AND Catchment_slope AND Topographic_Wetness_Index AND RelativeSlopePosition AND LS_Factor"
## [1] "Kappa overall =  0.999458070517194"
## [1] "Tau overall =  0.999460884149994"
## [1] "mean quality =  0.999154478701509"
## [1] "The quality is  0.999154478701509"
## [1] "#########  Cramer's V =  0.999553021648355"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   39   3   0   5   0   8   2   0   0   0   4   9   0   0   0   0
##      Ant   5  86   0   4   0   1   1   3   0   0   1   2   0   4   0   2
##      CBD   0   0  69   0   5   0   0   0   6   0   0   0   6   3  13   0
##      CD   11   0   0  47   0  11   6   2   0   1   5   0   3   1   1   1
##      CSR   0   0   5   0  77   0   0   0   9   0   0   0   1   3   3   2
##      DC    0   2   0   7   0  69   5   0   0   4   3   2   3   1   2   0
##      GLD   7   1   0  11   0   4 167   9   0   0   7   0   4   7   0   7
##      IMS   1   1   1   5   0   0   6  81   1   0   4   0   2   3   2   0
##      ISR   0   0   2   0   3   0   0   0  66   0   0   0   3   1  10   7
##      LD    0   0   0   4   0   0   1   0   0  82   5   0   0   6   8   0
##      LT    4   2   0   6   0   1   2   0   0   1  57   0   2   9   1   0
##      MrD  13   4   0   0   0   1   3   0   0   0   0  70   0   0   0   0
##      MxD   4   0  10   5   0   3   2   1   6   1   0   0  61   2   5   0
##      SB    1   0   2   0   0   0   1   1   0   4   7   0   2  44  11   2
##      SD    0   0   7   0   5   0   1   0   6   3   2   0   5   7  35   0
##      SSR   0   0   0   4   1   2   0   0   3   0   0   0   1   2   2  78
##      TG    3   0   5   3   0   0   2   0   3   4   5   0   7   5   7   1
##      WB    3   0   0   0   0   0   1   0   0   0   0   1   0   2   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD   1   0
##      CD    4   0
##      CSR   0   0
##      DC   12   0
##      GLD   5   1
##      IMS   3   0
##      ISR  10   0
##      LD    3   0
##      LT    5   1
##      MrD   0   1
##      MxD   8   0
##      SB    5   1
##      SD    2   0
##      SSR   3   0
##      TG  140   0
##      WB    0  96
## [1] "classification error rate with altdata:  0.305852417302799"
## [1] "Kappa overall =  0.674331995416797"
## [1] "Tau overall =  0.67615626403233"
## [1] "mean quality =  0.535103192109971"
## [1] "The quality is  0.535103192109971"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.68988604045021"

ROUGHNESS

SVM n=100

##  [1] "Prediction error at end is:  0.801237212442229"
##  [2] "Prediction error at end is:  0.713558705925118"
##  [3] "Prediction error at end is:  0.663105623928961"
##  [4] "Prediction error at end is:  0.646815443734746"
##  [5] "Prediction error at end is:  0.642219712312406"
##  [6] "Prediction error at end is:  0.639155891364179"
##  [7] "Prediction error at end is:  0.645277042114556"
##  [8] "Prediction error at end is:  0.638648283740977"
##  [9] "Prediction error at end is:  0.637631770265358"
## [10] "Prediction error at end is:  0.637626577348497"
##                            k 1                         k 2
## 1                  TRI_hr_ws26                 TRI_hr_ws22
## 2                      Texture                     Texture
## 3                fischerk_ws59               fischerk_ws59
## 4  terraintexture_hr_ws37_tp25 terraintexture_hr_ws57_tp25
## 5     vectorruggedness_hr_ws49    vectorruggedness_hr_ws25
## 6     vectorruggedness_hr_ws59                 TRI_hr_ws26
## 7  terraintexture_hr_ws57_tp25               fischerk_ws31
## 8     vectorruggedness_hr_ws15  terraintexture_hr_ws13_tp5
## 9                  TRI_hr_ws24 terraintexture_hr_ws21_tp25
## 10                 TRI_hr_ws15                 TRI_hr_ws24
##                            k 3                         k 4
## 1                  TRI_hr_ws26                 TRI_hr_ws24
## 2                      Texture                     Texture
## 3                fischerk_ws43               fischerk_ws59
## 4  terraintexture_hr_ws45_tp25 terraintexture_hr_ws53_tp25
## 5     vectorruggedness_hr_ws59    vectorruggedness_hr_ws19
## 6                fischerk_ws21    vectorruggedness_hr_ws53
## 7                fischerk_ws55      vectorstrength_hr_ws59
## 8     vectorruggedness_hr_ws17                fischerk_ws9
## 9     vectorruggedness_hr_ws53                 TRI_hr_ws26
## 10  terraintexture_hr_ws41_tp5    vectorruggedness_hr_ws59
##                            k 5
## 1                  TRI_hr_ws26
## 2                      Texture
## 3                fischerk_ws33
## 4  terraintexture_hr_ws57_tp25
## 5     vectorruggedness_hr_ws59
## 6     vectorruggedness_hr_ws15
## 7                 fischerk_ws7
## 8     vectorruggedness_hr_ws47
## 9                  TRI_hr_ws18
## 10                 TRI_hr_ws25

##                      allchosen Freq
## 16                     Texture    5
## 22                 TRI_hr_ws26    5
## 30    vectorruggedness_hr_ws59    4
## 6                fischerk_ws59    3
## 15 terraintexture_hr_ws57_tp25    3
## 20                 TRI_hr_ws24    3
## 23    vectorruggedness_hr_ws15    2
## 29    vectorruggedness_hr_ws53    2
## [1] "10fold cv-error:  0.623343527013252  for predictors TRI_hr_ws26 AND Texture AND fischerk_ws59 AND terraintexture_hr_ws57_tp25 AND vectorruggedness_hr_ws59"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   14   4   0   0   0   0   0   0   0   0   0   4   0   0   0   0   0
##   Ant   3  21   0   1   1   1   5   2   0   1   0   0   0   7   0   2   4
##   CBD   3   0  57   1   9   0   0   4  19  11   0   0   9   7  26  10   3
##   CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   CSR   0   0   8   0  70   0   0   0   3   4   0   0   0  14   4   2   1
##   DC    2   0   0  10   0  56   1   7   0   1   2   4   2   0   0   0   6
##   GLD  35  34   0  67   2  33 156  45   5  22  84   5  50  27  14  18  55
##   IMS   1   1   6   4   1   0   1  25   0   0   1   0   5   3   2   2   5
##   ISR   0   1   5   3   2   0   4   2  40   1   0   0   7   4   9   5   4
##   LD    1   8   0   4   0   0   3   1   0  44   2   0   1   1   1   3   4
##   LT    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   MrD   8  14   0   7   0   3   9   0   0   3   3  32   0   1   0   0   1
##   MxD   1   3   3   1   0   1   0   6   4   1   1   0  12   1   3   2   6
##   SB    1   0   0   0   1   0   3   0   1   1   0   0   0  17   2   5   0
##   SD    2   1   3   0   2   0   1   0   5   5   3   0   4   8  29   5   3
##   SSR   0   0   1   2   3   1   7   3  12   0   1   0   1   8   4  45   2
##   TG    2   2  18   1   0   5   4   2  11   6   1   0   9   2   6   1 106
##   WB   17  10   0   0   0   0   6   0   0   0   2  38   0   0   0   0   0
##      
## preds  WB
##   AD    2
##   Ant   1
##   CBD   0
##   CD    0
##   CSR   0
##   DC    0
##   GLD   5
##   IMS   0
##   ISR   0
##   LD    0
##   LT    0
##   MrD   4
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB   88
## [1] "Kappa overall =  0.368450928779918"
## [1] "Tau overall =  0.37938478143551"
## [1] "mean quality =  0.248452555581233"
## [1] "The quality is  0.248452555581233"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   31  12   0   0   0   0   0   0   0   0   0  18   0   5   0   0
##      Ant   2  37   0   1   1   2   8   4   1   5   6   0   0   7   9   4
##      CBD   4   0 110   0  25   0   0   4  42  24   1   0  20  13  63  10
##      CD    0   0   0   0   0   0   0   0   0   1   0   0   0   0   0   0
##      CSR   0   0  14   0  96   0   0   0   6   3   0   0   0  41   9   4
##      DC   15   0   0  13   0 115  13   6   0   4   6   5   3   0   2   0
##      GLD  58  86   4 137   4  63 283  98   8  48 141  21  92  43  19  23
##      IMS   6   2  11   5   1   1   6  48   3   2   5   0   7  18   6  15
##      ISR   0   2  14   7  12   1  21   3  60   0   3   0  24   9  17  15
##      LD    5   8   0   5   1   4   8   4   0  82   7   0   1   5   1   8
##      LT    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      MrD  22  14   0  19   0   3  25   0   0   2  11  63   0   1   0   0
##      MxD   1   4   7   5   0   3   4   9  16   1   2   0  22   4   7   5
##      SB    3   0   1   1   8   0   1   0   2   3   0   0   0  13   1   5
##      SD    0   2   1   1   5   0   2   1  20  11   1   0   3  12  34  28
##      SSR   3   3   0   2  17   0  11  12  30   2   1   0   8  17  10  70
##      TG    0   4  37   4   3   8   6   7  11  12   7   0  18   5  13  11
##      WB   36  23   0   2   0   0  13   1   0   0   9  70   0   5   0   1
##         
## altpreds  TG  WB
##      AD    0   2
##      Ant   6   2
##      CBD  17   0
##      CD    0   0
##      CSR   0   0
##      DC    7   0
##      GLD 105  10
##      IMS   8   0
##      ISR  10   0
##      LD    7   1
##      LT    0   0
##      MrD   4  16
##      MxD   9   0
##      SB    0   0
##      SD    2   0
##      SSR  10   0
##      TG  214   0
##      WB    1 170
## [1] "classification error rate with altdata:  0.630423685553854"
## [1] "Kappa overall =  0.321238957418729"
## [1] "Tau overall =  0.332492568237096"
## [1] "mean quality =  0.2088147732122"
## [1] "The quality is  0.2088147732122"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

SVM n=200

##  [1] "Prediction error at end is:  0.81622911616754" 
##  [2] "Prediction error at end is:  0.703674055829228"
##  [3] "Prediction error at end is:  0.665644955300128"
##  [4] "Prediction error at end is:  0.644461060286183"
##  [5] "Prediction error at end is:  0.628894297182474"
##  [6] "Prediction error at end is:  0.633229728673078"
##  [7] "Prediction error at end is:  0.635529556650246"
##  [8] "Prediction error at end is:  0.633230706075534"
##  [9] "Prediction error at end is:  0.632206388302447"
## [10] "Prediction error at end is:  0.633994708994709"
##                            k 1                         k 2
## 1                  TRI_hr_ws26                 TRI_hr_ws25
## 2                      Texture                     Texture
## 3                fischerk_ws59               fischerk_ws45
## 4     vectorruggedness_hr_ws57    vectorruggedness_hr_ws59
## 5  terraintexture_hr_ws57_tp25 terraintexture_hr_ws57_tp25
## 6                   TRI_hr_ws2               fischerk_ws19
## 7                fischerk_ws31    vectorruggedness_hr_ws43
## 8     vectorruggedness_hr_ws35                 TRI_hr_ws13
## 9  terraintexture_hr_ws53_tp25               fischerk_ws55
## 10    vectorruggedness_hr_ws59               fischerk_ws11
##                            k 3                         k 4
## 1                  TRI_hr_ws25                     Texture
## 2                      Texture                 TRI_hr_ws26
## 3                fischerk_ws61               fischerk_ws53
## 4  terraintexture_hr_ws57_tp25 terraintexture_hr_ws57_tp25
## 5     vectorruggedness_hr_ws57    vectorruggedness_hr_ws43
## 6                fischerk_ws29  terraintexture_hr_ws45_tp5
## 7   terraintexture_hr_ws41_tp5    vectorruggedness_hr_ws53
## 8     vectorruggedness_hr_ws49                fischerk_ws7
## 9                  TRI_hr_ws19                 TRI_hr_ws11
## 10                 TRI_hr_ws21               fischerk_ws61
##                            k 5
## 1                  TRI_hr_ws26
## 2                      Texture
## 3                fischerk_ws57
## 4  terraintexture_hr_ws57_tp25
## 5     vectorruggedness_hr_ws57
## 6   terraintexture_hr_ws49_tp5
## 7                 fischerk_ws7
## 8  terraintexture_hr_ws49_tp25
## 9     vectorruggedness_hr_ws59
## 10      vectorstrength_hr_ws57

##                      allchosen Freq
## 17 terraintexture_hr_ws57_tp25    5
## 18                     Texture    5
## 25                 TRI_hr_ws26    3
## 30    vectorruggedness_hr_ws57    3
## 31    vectorruggedness_hr_ws59    3
## 10               fischerk_ws61    2
## 11                fischerk_ws7    2
## 24                 TRI_hr_ws25    2
## 27    vectorruggedness_hr_ws43    2
## [1] "10fold cv-error:  0.619703930576825  for predictors TRI_hr_ws26 AND Texture AND fischerk_ws59 AND terraintexture_hr_ws57_tp25 AND vectorruggedness_hr_ws59"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   49   2   0   1   0   0   2   0   0   0   0  26   0   6   0   0   0
##   Ant   3  59   0   2   3   2  13   2   0   1   7   2   0  10   7   1   7
##   CBD   4   0 117   1  25   0   0   3  37  25   1   0  26  13  63  14  11
##   CD    0   0   0   1   0   0   0   0   0   0   1   0   0   0   0   0   0
##   CSR   0   0   6   0 104   0   0   0   4   2   0   0   0  34  10   2   0
##   DC   21   0   1  28   0 120  16   7   0   4  10   3   4   0   1   0  13
##   GLD  47  71   4 123   5  54 268  97   5  38 125  14  84  36  20  14  99
##   IMS  13   7  11   7   1   1  15  70  12   6   7   0  18  15   8  11  13
##   ISR   0   1  14  11  10   1  18   2  83   1   4   0  24   7  15  22  16
##   LD    6  13   0   4   1   5  12   0   0  99  12   0   2   7   2  12   9
##   LT    0   3   0   0   0   0   0   0   0   0   2   0   0   0   0   0   0
##   MrD  17  19   0  15   0   7  28   0   0   1  13  82   0   1   0   0   3
##   MxD   0   3   9   1   0   1   2   1   5   2   1   0  14   3   6   1   3
##   SB    2   0   1   2   5   0   1   1   2   1   1   0   2  28   4   7   0
##   SD    1   0   4   0   1   0   1   1  12   9   2   0   4  13  28  11   2
##   SSR   1   3   1   2  16   0   9  10  22   3   3   0   4  15  15  94  12
##   TG    1   2  31   2   2   9   4   3  17   8   3   0  16   6  12  10 210
##   WB   21  14   0   2   0   0  12   0   0   0   8  50   0   4   0   0   2
##      
## preds  WB
##   AD    7
##   Ant   0
##   CBD   0
##   CD    0
##   CSR   0
##   DC    0
##   GLD  11
##   IMS   0
##   ISR   0
##   LD    2
##   LT    0
##   MrD  20
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB  161
## [1] "Kappa overall =  0.361383942396621"
## [1] "Tau overall =  0.370597243491577"
## [1] "mean quality =  0.242001039865342"
## [1] "The quality is  0.242001039865342"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.428566107151365"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   21   4   0   0   0   0   1   0   0   0   0  10   0   0   0   0
##      Ant   4  32   0   3   1   2  10   2   0   0   3   1   0   6   1   0
##      CBD   2   0  59   1  10   0   0   3  25  12   2   0  11   7  31  13
##      CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CSR   0   0   8   0  64   0   0   0   3   3   0   0   0  20   4   1
##      DC    5   0   0  14   0  60   5   7   0   1   6   4   2   0   0   0
##      GLD  31  26   0  63   2  29 136  43   3  20  77   4  48  23  11  17
##      IMS   1   1   6   3   1   0   7  33   6   1   1   0   9   6   5   3
##      ISR   0   0   7   2   4   0   5   1  33   1   0   0   9   7  11  10
##      LD    2   9   0   2   0   2   7   1   0  49   2   0   1   2   1   4
##      LT    0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      MrD  10  13   0   9   0   1  10   0   0   0   2  38   0   1   0   0
##      MxD   0   1   5   1   0   0   0   2   3   1   0   0   5   0   1   1
##      SB    1   0   0   0   2   0   3   0   2   0   0   0   1  11   3   8
##      SD    3   0   1   0   1   0   2   1   3   5   1   0   3   6  20   1
##      SSR   1   1   0   3   5   1   6   3  13   1   2   0   0   8   5  42
##      TG    1   2  15   0   1   5   2   1   9   6   2   0  11   3   7   0
##      WB    8   9   0   0   0   0   6   0   0   0   2  26   0   0   0   0
##         
## altpreds  TG  WB
##      AD    1   9
##      Ant   2   0
##      CBD   4   0
##      CD    0   0
##      CSR   2   0
##      DC    9   0
##      GLD  49   6
##      IMS   8   0
##      ISR   4   0
##      LD    8   0
##      LT    0   0
##      MrD   0   4
##      MxD   2   0
##      SB    0   0
##      SD    4   0
##      SSR   3   0
##      TG  104   0
##      WB    0  81
## [1] "classification error rate with altdata:  0.598369011213048"
## [1] "Kappa overall =  0.35670424411517"
## [1] "Tau overall =  0.366432811656773"
## [1] "mean quality =  0.239785824898968"
## [1] "The quality is  0.239785824898968"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

RandomForest n=100

importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=5,altdata=twohundred,legend=geolegendeng)
## [1] "OBB error with all predictors of  roughnesscols is  0.559979581419091"
##             MeanDecreaseGini  parameters
## Texture             59.49776     Texture
## TRI_hr_ws47         23.38630 TRI_hr_ws47
## TRI_hr_ws46         21.31146 TRI_hr_ws46
## TRI_hr_ws44         21.22743 TRI_hr_ws44
## TRI_hr_ws42         20.23423 TRI_hr_ws42
## TRI_hr_ws45         19.87236 TRI_hr_ws45
## TRI_hr_ws43         19.57957 TRI_hr_ws43
## TRI_hr_ws40         17.99350 TRI_hr_ws40
## TRI_hr_ws41         16.94739 TRI_hr_ws41
## TRI_hr_ws38         16.75318 TRI_hr_ws38
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   38   5   0   2   0   0   1   0   0   1   2  19   0   2   2   0
##      Ant  14  89   0  15   3   7  25   2   0   7  12   9   5  16   7   7
##      CBD   3   3  95   2  19   0   2   9  21  14   0   0  21  12  39  13
##      CD    4   1   0  24   0  13  10   1   0   3  11   3   5   0   0   3
##      CSR   0   0  13   0  98   0   0   1   6   2   0   0   0  37  10   2
##      DC   22   2   0  19   0 109  18   1   0   2  18   1   3   0   1   0
##      GLD  25  32   1  61   2  23 245  40   2  20  66   7  41  26   5  12
##      IMS   7   8   3  13   1   7  24  93   4   0   7   0  16   4   4   5
##      ISR   1   0  27   5  10   0  10   7  84   6   2   0  32  11  27  32
##      LD    6  11   2  12   0   8   9   0   2 101  14   0   3   8   7   5
##      LT    6   6   0  28   0  12  19   4   0   3  39   3  10   8   0   4
##      MrD  39  19   0   0   0   1   2   0   0   0   2 121   0   1   0   0
##      MxD   3   3   5   3   0  11  12  12  10   1   5   0  32   5   6   6
##      SB    5   6   1   3   6   0  10   5   5   4   4   0   1   9   9   6
##      SD    3   1  16   1   9   0   2   3  29  15   2   0   4  22  43  18
##      SSR   1   2   2   1  16   3   2   6  18   5   3   0   5   9  12  71
##      TG    4   5  34  12   6   4  10  13  17  15  13   0  20  28  16  15
##      WB    5   4   0   1   0   0   0   0   0   1   0  11   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   5   4
##      CBD  21   0
##      CD   11   0
##      CSR   3   0
##      DC    7   0
##      GLD  54   7
##      IMS  11   0
##      ISR  18   0
##      LD   14   3
##      LT   21   2
##      MrD   0   6
##      MxD   8   0
##      SB    4   0
##      SD    6   0
##      SSR   4   0
##      TG  212   0
##      WB    0 179
## [1] "classification error rate with altdata:  0.569270166453265"

##  [1] "Prediction error at end is:  0.81243573765384" 
##  [2] "Prediction error at end is:  0.668703588305551"
##  [3] "Prediction error at end is:  0.592257360959651"
##  [4] "Prediction error at end is:  0.569311159578335"
##  [5] "Prediction error at end is:  0.549935088539232"
##  [6] "Prediction error at end is:  0.554516539440204"
##  [7] "Prediction error at end is:  0.549415796853092"
##  [8] "Prediction error at end is:  0.545340655346108"
##  [9] "Prediction error at end is:  0.540751415069845"
## [10] "Prediction error at end is:  0.544822661889183"
##                            k 1                         k 2
## 1                  TRI_hr_ws24                 TRI_hr_ws24
## 2                      Texture                     Texture
## 3     vectorruggedness_hr_ws57    vectorruggedness_hr_ws55
## 4       vectorstrength_hr_ws59      vectorstrength_hr_ws57
## 5     vectorruggedness_hr_ws13 terraintexture_hr_ws45_tp25
## 6   terraintexture_hr_ws49_tp5               fischerk_ws11
## 7     terraintexture_hr_ws5_t1    vectorruggedness_hr_ws35
## 8  terraintexture_hr_ws37_tp25  terraintexture_hr_ws57_tp5
## 9     vectorruggedness_hr_ws27   terraintexture_hr_ws37_t1
## 10 terraintexture_hr_ws21_tp25   terraintexture_hr_ws17_t1
##                            k 3                         k 4
## 1                  TRI_hr_ws24                 TRI_hr_ws24
## 2                      Texture                     Texture
## 3     vectorruggedness_hr_ws57    vectorruggedness_hr_ws55
## 4       vectorstrength_hr_ws49      vectorstrength_hr_ws59
## 5  terraintexture_hr_ws29_tp25 terraintexture_hr_ws57_tp25
## 6   terraintexture_hr_ws57_tp5   terraintexture_hr_ws5_tp5
## 7       vectorstrength_hr_ws59    vectorruggedness_hr_ws23
## 8     vectorruggedness_hr_ws11      vectorstrength_hr_ws37
## 9       vectorstrength_hr_ws47    vectorruggedness_hr_ws57
## 10    vectorruggedness_hr_ws51                 TRI_hr_ws22
##                            k 5
## 1                  TRI_hr_ws24
## 2                      Texture
## 3     vectorruggedness_hr_ws53
## 4                fischerk_ws59
## 5  terraintexture_hr_ws53_tp25
## 6   terraintexture_hr_ws33_tp5
## 7     terraintexture_hr_ws5_t1
## 8     vectorruggedness_hr_ws15
## 9                fischerk_ws25
## 10   terraintexture_hr_ws57_t1

##                     allchosen Freq
## 18                    Texture    5
## 20                TRI_hr_ws24    5
## 30   vectorruggedness_hr_ws57    3
## 35     vectorstrength_hr_ws59    3
## 15 terraintexture_hr_ws57_tp5    2
## 16   terraintexture_hr_ws5_t1    2
## 29   vectorruggedness_hr_ws55    2
predict_ranfor_newlegend_full_naproblem(modeldata=onehundred,dependent="geomorphologie_beschreibung",predictors=c("TRI_hr_ws24","Texture","vectorruggedness_hr_ws57","vectorstrength_hr_ws59","terraintexture_hr_ws53_tp25"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.534658511722732  for predictors TRI_hr_ws24 AND Texture AND vectorruggedness_hr_ws57 AND vectorstrength_hr_ws59 AND terraintexture_hr_ws53_tp25"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   38   3   0   4   0   2   1   2   1   2   3  19   1   1   0   0
##      Ant  21 111   0   8   1   4  13   1   1   3  15  13   4   9   6   2
##      CBD   0   0  95   1  19   0   0   6  21  11   0   0  24  14  34   5
##      CD    7   5   2  47   0  15  22   2   0   5  14   1  12   5   3   1
##      CSR   0   0  14   0  92   0   0   0   7   2   0   0   0  34   8   5
##      DC   20   0   0  11   0 117  12   0   0   2  12   2   4   0   1   0
##      GLD  13  25   4  54   4  16 248  44   5  13  55  10  28  27  11   9
##      IMS   3   7  10   9   0   5  11  96   4   1   4   0  12   6   3  10
##      ISR   2   0  26   5   9   0  11   6  72   3   2   0  24  11  23  23
##      LD    7   8   3   8   4   8  13   2   2 119  13   0   4   5   5   7
##      LT    7   5   0  28   1  12  25   7   0   5  38   1  15   4   0   3
##      MrD  34  17   0   2   0   2   6   0   0   0   0 117   0   0   0   0
##      MxD   6   6   7  10   1   4   8   8  19   2  11   0  38   4  16  10
##      SB    5   1   4   3  16   0   9   8   3   4   9   2   5  30  10  10
##      SD    5   1  11   2   7   1   5   4  26  14   4   0   7  20  51  22
##      SSR   5   5   2   3  18   1  10   8  27   5   6   0   7  12   9  84
##      TG    3   0  21   7   1  12   6   3  11   9  13   0  13  14  11   8
##      WB   10   3   0   0   0   1   1   0   0   0   1  12   0   2   0   0
##         
## altpreds  TG  WB
##      AD    1   3
##      Ant  10   2
##      CBD  11   0
##      CD   13   2
##      CSR   0   0
##      DC   14   0
##      GLD  35   5
##      IMS   6   0
##      ISR  10   0
##      LD   18   3
##      LT   18   8
##      MrD   0   8
##      MxD  17   0
##      SB    7   1
##      SD   15   0
##      SSR   8   0
##      TG  216   0
##      WB    1 169
## [1] "classification error rate with altdata:  0.546197039305768"
## [1] "Kappa overall =  0.417306507929602"
## [1] "Tau overall =  0.42167372308801"
## [1] "mean quality =  0.297114764397189"
## [1] "The quality is  0.297114764397189"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.469397029080755"

RandomForest n=200

##  [1] "Prediction error at end is:  0.816234003179816"
##  [2] "Prediction error at end is:  0.664116949461777"
##  [3] "Prediction error at end is:  0.586010438658222"
##  [4] "Prediction error at end is:  0.554108022519353"
##  [5] "Prediction error at end is:  0.520929444574765"
##  [6] "Prediction error at end is:  0.514292230302082"
##  [7] "Prediction error at end is:  0.512761943858003"
##  [8] "Prediction error at end is:  0.519653608569865"
##  [9] "Prediction error at end is:  0.519146336695598"
## [10] "Prediction error at end is:  0.511999244142101"
##                            k 1                         k 2
## 1                      Texture                     Texture
## 2                  TRI_hr_ws26                 TRI_hr_ws22
## 3     vectorruggedness_hr_ws59    vectorruggedness_hr_ws59
## 4                fischerk_ws61               fischerk_ws61
## 5  terraintexture_hr_ws57_tp25 terraintexture_hr_ws53_tp25
## 6   terraintexture_hr_ws57_tp5      vectorstrength_hr_ws31
## 7       vectorstrength_hr_ws23  terraintexture_hr_ws57_tp5
## 8                   TRI_hr_ws6  terraintexture_hr_ws49_tp5
## 9     vectorruggedness_hr_ws35 terraintexture_hr_ws25_tp25
## 10    terraintexture_hr_ws9_t1   terraintexture_hr_ws41_t1
##                            k 3                         k 4
## 1                      Texture                     Texture
## 2                  TRI_hr_ws26                 TRI_hr_ws23
## 3     vectorruggedness_hr_ws57    vectorruggedness_hr_ws55
## 4       vectorstrength_hr_ws61 terraintexture_hr_ws57_tp25
## 5  terraintexture_hr_ws53_tp25               fischerk_ws61
## 6     vectorruggedness_hr_ws37  terraintexture_hr_ws57_tp5
## 7   terraintexture_hr_ws57_tp5    vectorruggedness_hr_ws37
## 8    terraintexture_hr_ws41_t1                  TRI_hr_ws8
## 9                fischerk_ws39   terraintexture_hr_ws37_t1
## 10 terraintexture_hr_ws29_tp25 Melton_Ruggedness_Number_hr
##                            k 5
## 1                      Texture
## 2                  TRI_hr_ws25
## 3     vectorruggedness_hr_ws59
## 4       vectorstrength_hr_ws61
## 5  terraintexture_hr_ws57_tp25
## 6                fischerk_ws17
## 7    terraintexture_hr_ws49_t1
## 8   terraintexture_hr_ws57_tp5
## 9    terraintexture_hr_ws13_t1
## 10   terraintexture_hr_ws9_tp5

##                      allchosen Freq
## 14  terraintexture_hr_ws57_tp5    5
## 17                     Texture    5
## 3                fischerk_ws61    3
## 13 terraintexture_hr_ws57_tp25    3
## 28    vectorruggedness_hr_ws59    3
## 9    terraintexture_hr_ws41_t1    2
## 12 terraintexture_hr_ws53_tp25    2
## 21                 TRI_hr_ws26    2
## 25    vectorruggedness_hr_ws37    2
## 31      vectorstrength_hr_ws61    2
predict_ranfor_newlegend_full_naproblem(modeldata=onehundred,dependent="geomorphologie_beschreibung",predictors=c("TRI_hr_ws26","Texture","vectorruggedness_hr_ws57","fischerk_ws61","terraintexture_hr_ws53_tp25"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.528542303771662  for predictors TRI_hr_ws26 AND Texture AND vectorruggedness_hr_ws57 AND fischerk_ws61 AND terraintexture_hr_ws53_tp25"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   40   1   0   7   0   1   0   2   1   2   4  20   1   3   0   0
##      Ant  18 105   0   7   2   5  15   2   1   5  15  12   5   7   6   2
##      CBD   1   0  94   0  18   0   1   7  24   9   0   0  20  13  32   6
##      CD    7   8   2  49   0  16  20   1   0   4   9   1  10   4   3   1
##      CSR   0   0  15   0  92   0   0   0   7   2   0   0   0  33   6   5
##      DC   24   0   0  11   0 120  13   0   0   1  10   2   4   0   1   0
##      GLD  12  27   4  50   3  12 240  41   6  15  55  10  30  26   9   8
##      IMS   3   6   9   9   1   5   9  99   4   1   6   0  14  10   2  11
##      ISR   1   0  23   4   8   1  14   5  76   1   2   0  29  11  25  25
##      LD    4   7   4   6   4   8  15   2   1 121  14   0   4   4   6   7
##      LT    7   8   0  28   1  17  27   8   0   5  40   1  14   4   0   1
##      MrD  34  15   0   1   0   1   4   0   0   0   1 117   0   0   0   0
##      MxD   6   6   8  11   0   4  11  10  14   2  10   0  34   5  20   9
##      SB    5   2   6   3  16   0  10   4   6   4  10   3   4  30  11   9
##      SD    6   2  10   2   9   1   2   3  23  15   4   0   6  20  50  23
##      SSR   4   6   3   4  18   1  12   9  25   4   2   0   8  15  12  83
##      TG    4   0  21   8   1   8   6   4  11   9  17   0  15  11   8   9
##      WB   10   4   0   2   0   0   2   0   0   0   1  11   0   2   0   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   8   3
##      CBD  11   0
##      CD   10   1
##      CSR   0   0
##      DC   14   0
##      GLD  36   5
##      IMS   7   0
##      ISR   8   0
##      LD   17   3
##      LT   18   7
##      MrD   0  14
##      MxD  17   0
##      SB    8   1
##      SD   16   0
##      SSR  12   0
##      TG  216   1
##      WB    1 166
## [1] "classification error rate with altdata:  0.547728432873915"
## [1] "Kappa overall =  0.415804167759251"
## [1] "Tau overall =  0.420052247545266"
## [1] "mean quality =  0.296344263886633"
## [1] "The quality is  0.296344263886633"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.468545035166489"

RLI

SVM n=100

##  [1] "Prediction error at end is:  0.889981565145142"
##  [2] "Prediction error at end is:  0.831887105987433"
##  [3] "Prediction error at end is:  0.80692994755154" 
##  [4] "Prediction error at end is:  0.784513423690087"
##  [5] "Prediction error at end is:  0.764649218466012"
##  [6] "Prediction error at end is:  0.758525471257205"
##  [7] "Prediction error at end is:  0.75700524484603" 
##  [8] "Prediction error at end is:  0.754459417354728"
##  [9] "Prediction error at end is:  0.746818040193177"
## [10] "Prediction error at end is:  0.747828062522719"
##                                                  k 1
## 1        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2        geom_hr_L50m_fl1_r_li_simpson_UE_hr_10cells
## 3     geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 4  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 5       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 6        geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 7       geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 8   geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 9         geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 10         geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
##                                                 k 2
## 1        geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## 2  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 3      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4   geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 5    geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 6       geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 7       geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 8        geom_hr_L3_fl10_r_li_simpson_UE_hr_10cells
## 9     geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 10        geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
##                                                  k 3
## 1        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2    geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## 3       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4        geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 5        geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 6  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 7   geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 8          geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 9      geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## 10        geom_hr_L3_fl1_r_li_richness_UE_hr_10cells
##                                                 k 4
## 1  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 2       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 3      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4  geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 5       geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 6         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 7      geom_hr_L50m_fl1_r_li_patchnum_UE_hr_20cells
## 8  geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_10cells
## 9             geom_hr_L3_fl1_r_li_mps_UE_hr_10cells
## 10     geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
##                                                 k 5
## 1       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2   geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## 3       geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 4      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5  geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 6         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 7    geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 8    geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 9     geom_hr_L50m_fl1_r_li_dominance_UE_hr_20cells
## 10       geom_hr_L3_fl1_r_li_richness_UE_hr_20cells

##                                            allchosen Freq
## 1       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells    5
## 6        geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells    5
## 3        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells    4
## 16  geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells    4
## 9     geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells    3
## 12         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells    3
## 11        geom_hr_L3_fl1_r_li_richness_UE_hr_20cells    2
## 13         geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells    2
## 17 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells    2
## 20   geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells    2
## 21  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells    2
## 23      geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells    2
## 24       geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells    2
## [1] "10fold cv-error:  0.769740193581253  for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells AND geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   Ant   1   3   0   0   1   0   0   0   0   0   0   1   0   0   0   0   0
##   CBD   0   0   3   0   0   4   1   0   5   1   1   0   0   3   1   2   2
##   CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   CSR   5   2  20   3  53   7  11   2  12   8   7   0   4  15  24   3   6
##   DC    3   2   2  13   3  21   7   3   2   6   8   0   1   1   2   1   4
##   GLD  18  19   7  38   2  16  85  31   2  12  41   4  27  25   6  12  32
##   IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   ISR   2   2  27   9   8  13  11   8  38  14   3   1  14  11  21  25  23
##   LD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   LT    1   1   0   2   1   5   2   0   0   0   8   0   4   1   0   0   3
##   MrD  31  20   0   4   0   2  19   0   0   0   1  67   0   0   0   0   0
##   MxD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   SB    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   SD    1   1   8   0   7   2   2   1   5   1   3   0   1   6  12  10   1
##   SSR   0   1   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   TG   18  44  34  30  16  20  58  52  36  58  23   1  48  38  34  47 127
##   WB   10   4   0   2   0  10   4   0   0   0   5  10   1   0   0   0   2
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    0
##   CSR   0
##   DC    3
##   GLD   9
##   IMS   0
##   ISR   0
##   LD    0
##   LT    0
##   MrD   7
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB   81
## [1] "Kappa overall =  0.19085277704154"
## [1] "Tau overall =  0.209792933984597"
## [1] "mean quality =  0.110356921968066"
## [1] "The quality is  0.110356921968066"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      Ant   0   1   0   1   0   0   0   0   0   2   0   0   0   0   0   0
##      CBD   5   0   4   2   3   6   4   1   2   3   2   0   5   4  10   6
##      CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CSR   7   4  31  10  61   8  25   7  23  22   5   0   8  34  37  13
##      DC   14  12   4  31   4  19  20   8   5  11  19   3   5   3   8   4
##      GLD  27  30  14  59  11  45 147  62   5  44  59  13  56  32  16  31
##      IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      ISR   6   3  50  11  39  22  23  28  66  13  12   0  37  27  50  44
##      LD    0   1   0   0   0   0   1   0   0   1   1   0   0   0   0   0
##      LT    1   6   0   6   1  13   9   0   2   1   9   0   1   0   1   1
##      MrD  55  37   0  10   0   8  42   0   0   1  11 137   4   3   0   0
##      MxD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SB    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SD    1   0  16   2  19   5   7   0  10   7   2   0   6  20  17  14
##      SSR   0   0   2   0   0   0   1   1   1   0   0   0   0   0   1   1
##      TG   41 100  78  67  35  57 112  90  85  95  72   2  76  75  51  85
##      WB   29   3   0   3   0  17  10   0   0   0   8  23   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   2   1
##      CBD   4   0
##      CD    0   0
##      CSR  17   0
##      DC   10   3
##      GLD  72  10
##      IMS   0   0
##      ISR  36   0
##      LD    0   0
##      LT    5   2
##      MrD   2   6
##      MxD   0   0
##      SB    0   0
##      SD    3   0
##      SSR   0   0
##      TG  249  10
##      WB    0 169
## [1] "classification error rate with altdata:  0.775197754529217"
## [1] "Kappa overall =  0.159676428839987"
## [1] "Tau overall =  0.1792023775573"
## [1] "mean quality =  0.0943794212714953"
## [1] "The quality is  0.0943794212714953"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

SVM n=200

##  [1] "Prediction error at end is:  0.880581945421847"
##  [2] "Prediction error at end is:  0.821640667761357"
##  [3] "Prediction error at end is:  0.791527223916387"
##  [4] "Prediction error at end is:  0.77111091563062" 
##  [5] "Prediction error at end is:  0.757076067975083"
##  [6] "Prediction error at end is:  0.755287095681184"
##  [7] "Prediction error at end is:  0.748912476868142"
##  [8] "Prediction error at end is:  0.741258764042015"
##  [9] "Prediction error at end is:  0.735134686058331"
## [10] "Prediction error at end is:  0.731306200641176"
##                                                  k 1
## 1      geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 2          geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3         geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## 4       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5   geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 6        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 7  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 8        geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 9         geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 10      geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
##                                                  k 2
## 1  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 2          geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 3       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4         geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 5         geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 6       geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 7   geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 8       geom_hr_L50m_fl10_r_li_simpson_UE_hr_10cells
## 9       geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells
## 10   geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
##                                                  k 3
## 1       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 2          geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3   geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 4    geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 5  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 6       geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells
## 7        geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 8       geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 9         geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 10       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
##                                                  k 4
## 1       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 2   geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 3          geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 4  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 5    geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 6     geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 7         geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 8        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 9         geom_hr_L3_fl10_r_li_simpson_UE_hr_10cells
## 10     geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
##                                                  k 5
## 1        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2          geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 3       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4   geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 5  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 6     geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 7             geom_hr_L3_fl10_r_li_mps_UE_hr_10cells
## 8         geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 9        geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 10      geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells

##                                            allchosen Freq
## 1       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells    5
## 14 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells    5
## 19  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells    5
## 4        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells    4
## 10        geom_hr_L3_fl1_r_li_richness_UE_hr_20cells    4
## 20      geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells    4
## 12         geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells    3
## 3    geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells    2
## 5         geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells    2
## 8        geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells    2
## 11         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells    2
## 13    geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells    2
## 17      geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells    2
## [1] "10fold cv-error:  0.777749425873947  for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells AND geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD    5   0   0   0   0   1   1   0   0   0   0   1   0   0   0   0   0
##   Ant   1   4   0   0   0   0   0   0   0   1   0   0   0   0   0   0   0
##   CBD   0   0  21   0  10   5   4   2   6   6   3   0   6   4   5   8   1
##   CD    0   3   0   9   0   5   3   0   1   1   4   1   1   0   0   0   3
##   CSR   7   2  22  12  50   6  16   6  16  17   7   0   7  28  24   5  15
##   DC    3   4   1   2   2   4   4   0   1   0   0   1   1   1   3   1   2
##   GLD  50  57  18 102  22  76 214  81  14  54  95  28  72  39  20  36  93
##   IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   ISR   3   2  28  13  23  24  19  30  62   7   7   0  35  24  29  32  35
##   LD    0   2   1   2   1   2   2   1   0   6   7   0   1   3   0   1   4
##   LT    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   MrD  50  30   0   3   0   6  18   0   0   0   6 125   1   3   0   0   1
##   MxD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   SB    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   SD    8   1  36   8  36  13  20   4  26  16   5   0  10  32  54  31  10
##   SSR   0   0   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0
##   TG   39  90  72  48  29  42  90  73  73  92  59   0  64  64  56  85 236
##   WB   20   2   0   3   0  16   9   0   0   0   7  22   0   0   0   0   0
##      
## preds  WB
##   AD    3
##   Ant   0
##   CBD   0
##   CD    1
##   CSR   0
##   DC    2
##   GLD  14
##   IMS   0
##   ISR   1
##   LD    1
##   LT    0
##   MrD   5
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG   11
##   WB  163
## [1] "Kappa overall =  0.177203088186952"
## [1] "Tau overall =  0.198655119103012"
## [1] "mean quality =  0.107043093789178"
## [1] "The quality is  0.107043093789178"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    2   1   0   0   0   0   1   0   0   0   1   6   1   0   0   0
##      Ant   0   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0
##      CBD   1   0   1   1   1   0   3   0   1   1   1   0   2   1   6   6
##      CD    0   2   0   4   0   4   1   0   1   2   0   0   0   0   0   0
##      CSR   3   2  11   5  41   6   9   1  12   6   4   0   3  11  15   0
##      DC    0   1   1   1   0   1   3   0   2   2   1   0   0   0   0   0
##      GLD  26  22  10  47   8  40 107  40   3  21  63  14  34  31   8  15
##      IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      ISR   1   3  19   8   9  14   7   8  33   7   2   1  14   6  12  26
##      LD    0   1   0   2   0   0   0   1   0   3   1   0   2   0   1   0
##      LT    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      MrD  31  17   0   2   0   2  13   0   0   0   0  58   0   0   0   0
##      MxD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SB    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SD    3   1  23   2  18   8   7   2  14   8   5   0   2  14  24  17
##      SSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      TG   17  47  36  26  14  15  46  45  34  50  18   2  42  37  34  36
##      WB    6   2   0   2   0  10   3   0   0   0   4   3   0   0   0   0
##         
## altpreds  TG  WB
##      AD    1   2
##      Ant   0   0
##      CBD   3   0
##      CD    0   0
##      CSR   3   0
##      DC    1   1
##      GLD  54  12
##      IMS   0   0
##      ISR  20   0
##      LD    1   0
##      LT    0   0
##      MrD   0   8
##      MxD   0   0
##      SB    0   0
##      SD    7   0
##      SSR   0   0
##      TG  109   0
##      WB    1  77
## [1] "classification error rate with altdata:  0.765664798777382"
## [1] "Kappa overall =  0.167535513331501"
## [1] "Tau overall =  0.189296095412184"
## [1] "mean quality =  0.101971529130827"
## [1] "The quality is  0.101971529130827"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

RandomForest n=100

importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=4,altdata=twohundred,legend=geolegendeng)
## [1] "OBB error with all predictors of  rlicols is  0.568577277379734"
##                                                  MeanDecreaseGini
## geom_hr_L3_fl10_r_li_simpson_UE_hr_60cells               22.29497
## geom_hr_L3_fl1_r_li_dominance_UE_hr_60cells              20.14078
## geom_hr_L3_fl1_r_li_edgedensity_UE_hr_60cells            20.12305
## geom_hr_L3_fl10_r_li_shannon_UE_hr_60cells               20.10577
## geom_hr_L3_fl1_r_li_patchnum_UE_hr_60cells               18.92739
## geom_hr_L50m_fl1_r_li_dominance_UE_hr_60cells            18.87843
## geom_hr_L3_fl1_r_li_shape_UE_hr_60cells                  18.85994
## geom_hr_L3_fl1_r_li_mps_UE_hr_60cells                    18.82247
## geom_hr_L3_fl1_r_li_patchdensity_UE_hr_60cells           18.73360
## geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_60cells         18.11850
##                                                                                        parameters
## geom_hr_L3_fl10_r_li_simpson_UE_hr_60cells             geom_hr_L3_fl10_r_li_simpson_UE_hr_60cells
## geom_hr_L3_fl1_r_li_dominance_UE_hr_60cells           geom_hr_L3_fl1_r_li_dominance_UE_hr_60cells
## geom_hr_L3_fl1_r_li_edgedensity_UE_hr_60cells       geom_hr_L3_fl1_r_li_edgedensity_UE_hr_60cells
## geom_hr_L3_fl10_r_li_shannon_UE_hr_60cells             geom_hr_L3_fl10_r_li_shannon_UE_hr_60cells
## geom_hr_L3_fl1_r_li_patchnum_UE_hr_60cells             geom_hr_L3_fl1_r_li_patchnum_UE_hr_60cells
## geom_hr_L50m_fl1_r_li_dominance_UE_hr_60cells       geom_hr_L50m_fl1_r_li_dominance_UE_hr_60cells
## geom_hr_L3_fl1_r_li_shape_UE_hr_60cells                   geom_hr_L3_fl1_r_li_shape_UE_hr_60cells
## geom_hr_L3_fl1_r_li_mps_UE_hr_60cells                       geom_hr_L3_fl1_r_li_mps_UE_hr_60cells
## geom_hr_L3_fl1_r_li_patchdensity_UE_hr_60cells     geom_hr_L3_fl1_r_li_patchdensity_UE_hr_60cells
## geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_60cells geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_60cells
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   29   5   0   0   0   0   1   0   0   0   0  15   1   0   0   0
##      Ant  19  99   0   6   1   2  16   1   4   9   3  11   4  10   6   0
##      CBD   2   0  68   2  19   0   0   1  15  12   4   0   4  11  24  11
##      CD    4   7   0  26   0  12  12   1   0   0   6   3   4   2   2   0
##      CSR   2   0  25   0  79   0   2   5  27  10   0   0  10  36  36  10
##      DC   17   2   1  36   0  78  14   1   0   4  24   0   1   3   0   0
##      GLD  29  26   1  85   5  28 256  48   2  15  80   5  42  29  13  13
##      IMS   4   4   1   4   1   2  11  85   0   0   3   1   5   7   1   3
##      ISR   0   0  25   4  27   0   9   4  86   2   5   0  36  11  26  45
##      LD    6  10   2   5   1   7   5   1   0  99   7   0   1   9   7   7
##      LT    7   3   0   6   2  28  16   0   0   0  23   1   8   3   4   0
##      MrD  41  17   0   1   0   4   9   0   0   0   4 128   0   0   0   0
##      MxD   3   0   5   6   1   9  11  11   8   0   8   0  27   4   6   3
##      SB    4   0   2   3   5   0   3   2   3   2   3   0   3   9   6   5
##      SD    0   0  22   1  11   0   2   0  11  14   0   0   2  11  29   9
##      SSR   0   2  15   3   7   1   9   2  17   2   2   0  10   7   7  43
##      TG    8  21  30  14  13  25  23  35  24  31  28   1  39  46  24  47
##      WB   11   1   0   0   0   3   2   0   0   0   0  12   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   1
##      Ant   4   6
##      CBD   8   0
##      CD   10   2
##      CSR   1   0
##      DC   16   1
##      GLD  51   9
##      IMS   6   0
##      ISR  23   0
##      LD    5   0
##      LT    5   0
##      MrD   0   2
##      MxD   9   0
##      SB    1   0
##      SD    4   0
##      SSR   6   0
##      TG  250   1
##      WB    0 176
## [1] "classification error rate with altdata:  0.592725409836066"

##  [1] "Prediction error at end is:  0.872634626369632"
##  [2] "Prediction error at end is:  0.835455938100431"
##  [3] "Prediction error at end is:  0.819146284467986"
##  [4] "Prediction error at end is:  0.807437555174742"
##  [5] "Prediction error at end is:  0.775849041906839"
##  [6] "Prediction error at end is:  0.743769797995534"
##  [7] "Prediction error at end is:  0.752921015734538"
##  [8] "Prediction error at end is:  0.739685568884042"
##  [9] "Prediction error at end is:  0.733059406968894"
## [10] "Prediction error at end is:  0.733568312821312"
##                                                 k 1
## 1       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2         geom_hr_L50m_fl1_r_li_shape_UE_hr_20cells
## 3  geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 4     geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 5      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 6        geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 7         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 8      geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 9    geom_hr_L3_fl10_r_li_edgedensity_UE_hr_20cells
## 10      geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
##                                                 k 2
## 1       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2       geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 3             geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 4        geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
## 5      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 6            geom_hr_L3_fl10_r_li_mps_UE_hr_20cells
## 7           geom_hr_L50m_fl1_r_li_mps_UE_hr_20cells
## 8  geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells
## 9        geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 10    geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
##                                                 k 3
## 1       geom_hr_L3_fl10_r_li_richness_UE_hr_10cells
## 2    geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 3      geom_hr_L3_fl10_r_li_dominance_UE_hr_20cells
## 4         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 5        geom_hr_L50m_fl10_r_li_shape_UE_hr_20cells
## 6      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 7          geom_hr_L3_fl10_r_li_shape_UE_hr_20cells
## 8        geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 9  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 10       geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
##                                             k 4
## 1   geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2     geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3         geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 4  geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5    geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 6      geom_hr_L3_fl10_r_li_shape_UE_hr_20cells
## 7   geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 8      geom_hr_L3_fl1_r_li_simpson_UE_hr_5cells
## 9  geom_hr_L50m_fl1_r_li_richness_UE_hr_10cells
## 10     geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells
##                                                 k 5
## 1       geom_hr_L3_fl10_r_li_richness_UE_hr_10cells
## 2             geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 3      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4        geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 5        geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 6       geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells
## 7       geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells
## 8        geom_hr_L50m_fl10_r_li_shape_UE_hr_20cells
## 9    geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 10 geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_10cells

##                                           allchosen Freq
## 2      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells    5
## 13       geom_hr_L3_fl1_r_li_richness_UE_hr_20cells    4
## 6       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells    3
## 7        geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells    3
## 11            geom_hr_L3_fl1_r_li_mps_UE_hr_20cells    3
## 5       geom_hr_L3_fl10_r_li_richness_UE_hr_10cells    2
## 8          geom_hr_L3_fl10_r_li_shape_UE_hr_20cells    2
## 10      geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells    2
## 14        geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells    2
## 18 geom_hr_L50m_fl10_r_li_edgedensity_UE_hr_20cells    2
## 20    geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells    2
## 21       geom_hr_L50m_fl10_r_li_shape_UE_hr_20cells    2
## 27      geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells    2
predict_ranfor_newlegend_full_naproblem(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("geom_hr_L3_fl10_r_li_richness_UE_hr_20cells","geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.79786150712831  for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"
## [1] "Kappa overall =  0.285943616058494"
## [1] "Tau overall =  0.299149394992213"
## [1] "mean quality =  0.179411764982154"
## [1] "The quality is  0.179411764982154"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.362649457558158"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    2   0   0   0   0   0   1   0   0   2   0   0   0   0   0   0
##      Ant   2   7   6   6   1   6  16  10   4  13   2   0   5   6   3   5
##      CBD   1   2   3   1   3   2   5   1   0   0   1   0   1   2   1   0
##      CD    2   5   2   5   0   6  13   8   1   8  10   0   7   4   0   7
##      CSR   4   5  50   6  75   2  27   7  39  24   6   9  14  54  61  27
##      DC   19  14   7  34  10  37  42  16  10  16  29   4  22  12  13  13
##      GLD  31  30  23  50  13  44  91  45  16  45  44   0  27  28  20  40
##      IMS   2   6   4   1   2   4   5   7   5   4   6   1   8   1   3   5
##      ISR   5  12  11  11  20   7  14  11  20  11  15   9  14  11  11   9
##      LD    0   5   6   1   1   1   5   6   1   3   0   1   3   1   1   8
##      LT    2   0   0   2   0   6  11   5   0   2   2   2   1   3   1   0
##      MrD  51  31   2  17   1  24  35   0   3   1  22 104   3   3   2   3
##      MxD   2   3   2   5   2   2   6   7   2   4   1   1   3   5   2   1
##      SB    4   0   5   0   7   0   2   0   1   1   0   0   0   1   6   1
##      SD    0   3   2   0   6   0   2   0   2   1   1   2   1   2   1   2
##      SSR   0   1   0   1   0   0   1   1   0   0   0   0   1   0   0   2
##      TG   27  59  76  52  34  31 103  73  95  65  49   6  85  65  66  76
##      WB   32  14   0  10   0  28  22   0   0   0  12  42   3   0   0   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   6   0
##      CBD   3   0
##      CD   10   2
##      CSR  18   2
##      DC   28   2
##      GLD  77   6
##      IMS  14   0
##      ISR  23   3
##      LD    2   0
##      LT    3   1
##      MrD   5  22
##      MxD   5   0
##      SB    0   0
##      SD    4   0
##      SSR   3   0
##      TG  191   5
##      WB    7 158
## [1] "classification error rate with altdata:  0.81855249745158"
## [1] "Kappa overall =  0.116912653239332"
## [1] "Tau overall =  0.133297355639504"
## [1] "mean quality =  0.0738530903316026"
## [1] "The quality is  0.0738530903316026"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.228346300619059"

RandomForest n=200

##  [1] "Prediction error at end is:  0.88569082805536" 
##  [2] "Prediction error at end is:  0.834908254489535"
##  [3] "Prediction error at end is:  0.81424140537441" 
##  [4] "Prediction error at end is:  0.802244767638856"
##  [5] "Prediction error at end is:  0.781320861678005"
##  [6] "Prediction error at end is:  0.764991073057576"
##  [7] "Prediction error at end is:  0.748661936038783"
##  [8] "Prediction error at end is:  0.745091810670628"
##  [9] "Prediction error at end is:  0.734885448432246"
## [10] "Prediction error at end is:  0.728506268407746"
##                                                  k 1
## 1        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2         geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 3        geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells
## 4      geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 5       geom_hr_L50m_fl10_r_li_simpson_UE_hr_20cells
## 6      geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## 7       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 8   geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 9              geom_hr_L3_fl1_r_li_mps_UE_hr_20cells
## 10 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
##                                                k 2
## 1      geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 2       geom_hr_L3_fl1_r_li_richness_UE_hr_20cells
## 3     geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4     geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 5      geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells
## 6  geom_hr_L3_fl10_r_li_patchdensity_UE_hr_20cells
## 7          geom_hr_L3_fl1_r_li_shape_UE_hr_20cells
## 8   geom_hr_L50m_fl10_r_li_dominance_UE_hr_20cells
## 9       geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 10       geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
##                                                k 3
## 1    geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells
## 2        geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 3     geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4   geom_hr_L3_fl1_r_li_patchdensity_UE_hr_20cells
## 5  geom_hr_L50m_fl1_r_li_edgedensity_UE_hr_20cells
## 6   geom_hr_L3_fl10_r_li_edgedensity_UE_hr_20cells
## 7      geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells
## 8     geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells
## 9         geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells
## 10         geom_hr_L3_fl10_r_li_shape_UE_hr_5cells
##                                                  k 4
## 1  geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells
## 2            geom_hr_L50m_fl1_r_li_mps_UE_hr_20cells
## 3       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 4      geom_hr_L50m_fl1_r_li_dominance_UE_hr_20cells
## 5          geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells
## 6        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 7             geom_hr_L3_fl10_r_li_mps_UE_hr_20cells
## 8       geom_hr_L50m_fl10_r_li_shannon_UE_hr_20cells
## 9         geom_hr_L3_fl1_r_li_richness_UE_hr_10cells
## 10        geom_hr_L3_fl10_r_li_simpson_UE_hr_20cells
##                                                 k 5
## 1          geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells
## 2  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells
## 3       geom_hr_L3_fl10_r_li_richness_UE_hr_20cells
## 4      geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells
## 5      geom_hr_L50m_fl10_r_li_shannon_UE_hr_20cells
## 6         geom_hr_L3_fl1_r_li_shannon_UE_hr_20cells
## 7        geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells
## 8         geom_hr_L50m_fl1_r_li_shape_UE_hr_20cells
## 9     geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells
## 10      geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells

##                                            allchosen Freq
## 1       geom_hr_L3_fl10_r_li_dominance_UE_hr_40cells    5
## 5        geom_hr_L3_fl10_r_li_richness_UE_hr_20cells    4
## 16         geom_hr_L3_fl1_r_li_simpson_UE_hr_20cells    3
## 6         geom_hr_L3_fl10_r_li_shannon_UE_hr_20cells    2
## 9      geom_hr_L3_fl1_r_li_edgedensity_UE_hr_20cells    2
## 13        geom_hr_L3_fl1_r_li_richness_UE_hr_20cells    2
## 18          geom_hr_L50m_fl10_r_li_mps_UE_hr_20cells    2
## 19 geom_hr_L50m_fl10_r_li_patchdensity_UE_hr_20cells    2
## 20     geom_hr_L50m_fl10_r_li_richness_UE_hr_20cells    2
## 21      geom_hr_L50m_fl10_r_li_shannon_UE_hr_20cells    2
## 26  geom_hr_L50m_fl1_r_li_patchdensity_UE_hr_20cells    2
## 27      geom_hr_L50m_fl1_r_li_richness_UE_hr_20cells    2
## 28       geom_hr_L50m_fl1_r_li_shannon_UE_hr_20cells    2
## 30       geom_hr_L50m_fl1_r_li_simpson_UE_hr_20cells    2
predict_ranfor_newlegend_full_naproblem(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("geom_hr_L3_fl10_r_li_richness_UE_hr_20cells","geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"),altdata=onehundred,legend=geolegendeng)
## [1] "OOB-error:  0.809123343527013  for predictors geom_hr_L3_fl10_r_li_richness_UE_hr_20cells AND geom_hr_L3_fl1_r_li_dominance_UE_hr_20cells"
## [1] "Kappa overall =  0.201828696032813"
## [1] "Tau overall =  0.220723151645979"
## [1] "mean quality =  0.116666594309716"
## [1] "The quality is  0.116666594309716"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.289069713308378"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    1   0   0   1   0   0   0   0   0   0   0   0   0   0   0   0
##      Ant   0   1   0   0   0   0   2   2   0   3   1   0   1   2   0   1
##      CBD   0   0   2   0   0   0   2   2   0   0   1   0   0   0   0   0
##      CD    0   0   0   0   0   3   1   0   1   0   1   0   1   0   0   0
##      CSR   8  11  37   9  68   9  23   5  33  22   8   6  11  30  43  18
##      DC    5   1   0   6   0   7  11   3   0   1   6   0   2   1   1   3
##      GLD  10  20  10  30   5  34  46  32   6  16  28   3  16  15  12  13
##      IMS   0   0   0   1   0   0   5   3   0   1   3   0   0   1   0   0
##      ISR   0   0   0   0   0   0   0   2   0   1   0   0   0   0   0   0
##      LD    2   2   3   1   0   0   4   2   1   2   0   0   2   3   0   0
##      LT    3   2   2   7   0   6   6   2   2   2   3   0   1   1   0   2
##      MrD  31  15   0   8   1  11  15   0   0   0   8  52   2   3   0   0
##      MxD   0   1   0   0   0   0   0   0   3   0   1   0   0   1   0   2
##      SB    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SSR   1   0   0   0   0   0   1   0   1   2   0   0   0   0   1   1
##      TG   15  39  47  33  17  17  74  44  53  50  25   6  61  42  43  60
##      WB   14   7   0   5   0  13  10   0   0   0  15  17   3   1   0   0
##         
## altpreds  TG  WB
##      AD    0   2
##      Ant   4   0
##      CBD   1   0
##      CD    0   0
##      CSR  14   1
##      DC    5   2
##      GLD  28   0
##      IMS   2   0
##      ISR   1   0
##      LD    1   0
##      LT    4   0
##      MrD   4  11
##      MxD   0   0
##      SB    0   0
##      SD    0   0
##      SSR   1   0
##      TG  133   2
##      WB    3  82
## [1] "classification error rate with altdata:  0.795824847250509"
## [1] "Kappa overall =  0.13711098087002"
## [1] "Tau overall =  0.157361926440637"
## [1] "mean quality =  0.0723346307152706"
## [1] "The quality is  0.0723346307152706"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

HEIGHTS

SVM n=100

##  [1] "Prediction error at end is:  0.738422391857506"
##  [2] "Prediction error at end is:  0.638167938931298"
##  [3] "Prediction error at end is:  0.590330788804071"
##  [4] "Prediction error at end is:  0.583715012722646"
##  [5] "Prediction error at end is:  0.557251908396947"
##  [6] "Prediction error at end is:  0.542493638676845"
##  [7] "Prediction error at end is:  0.540458015267176"
##  [8] "Prediction error at end is:  0.536895674300254"
##  [9] "Prediction error at end is:  0.530788804071247"
## [10] "Prediction error at end is:  0.525190839694657"
##                                     k 1
## 1                     Maximum_Height_hr
## 2                           ValleyDepth
## 3  Vertical_Distance_to_Channel_Network
## 4                           SlopeHeight
## 5                       Valley_Depth_hr
## 6                   Standardized_Height
## 7                     Mid_Slope_Positon
## 8                          Slope_Height
## 9                      NormalizedHeight
## 10               Standardized_Height_hr
##                                     k 2
## 1                     Maximum_Height_hr
## 2                           ValleyDepth
## 3  Vertical_Distance_to_Channel_Network
## 4                      NormalizedHeight
## 5                           SlopeHeight
## 6                       Valley_Depth_hr
## 7                     Normalized_Height
## 8                          Slope_Height
## 9                   Standardized_Height
## 10                    Mid_Slope_Positon
##                                     k 3
## 1                     Maximum_Height_hr
## 2                           ValleyDepth
## 3  Vertical_Distance_to_Channel_Network
## 4                           SlopeHeight
## 5                       Valley_Depth_hr
## 6                     Normalized_Height
## 7                      NormalizedHeight
## 8                          Slope_Height
## 9                       Slope_Height_hr
## 10                    Mid_Slope_Positon
##                                     k 4
## 1                     Maximum_Height_hr
## 2                           ValleyDepth
## 3  Vertical_Distance_to_Channel_Network
## 4                           SlopeHeight
## 5                       Valley_Depth_hr
## 6                   Standardized_Height
## 7                     Normalized_Height
## 8                          Slope_Height
## 9                Standardized_Height_hr
## 10                    Mid_Slope_Positon
##                                     k 5
## 1                     Maximum_Height_hr
## 2                           ValleyDepth
## 3  Vertical_Distance_to_Channel_Network
## 4                      NormalizedHeight
## 5                          Slope_Height
## 6                   Standardized_Height
## 7                       Valley_Depth_hr
## 8                     Mid_Slope_Positon
## 9                       Slope_Height_hr
## 10                          SlopeHeight

##                               allchosen Freq
## 1                     Maximum_Height_hr    5
## 2                     Mid_Slope_Positon    5
## 5                           SlopeHeight    5
## 6                          Slope_Height    5
## 10                          ValleyDepth    5
## 11                      Valley_Depth_hr    5
## 12 Vertical_Distance_to_Channel_Network    5
## 3                      NormalizedHeight    4
## 8                   Standardized_Height    4
## 4                     Normalized_Height    3
## 7                       Slope_Height_hr    2
## 9                Standardized_Height_hr    2
## [1] "10fold cv-error:  0.526208651399491  for predictors Maximum_Height_hr AND ValleyDepth AND Vertical_Distance_to_Channel_Network AND SlopeHeight AND Valley_Depth_hr AND Normalized_Height"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   36  18   0   5   0  12   3   3   0   0   1  28   0   0   0   0   0
##   Ant   0  18   0   0   0   0   3   0   0   0   3   0   0   3   0   0   0
##   CBD   0   0  72   0  10   0   0   2   3   2   1   0   1   2  13   0   9
##   CD    1   0   0  11   0   2   1   1   1   4   1   0   9   5   2   0   4
##   CSR   0   0   2   0  75   0   0   0   0   0   0   0   1   0   7   0   0
##   DC    3   6   3   4   0  22   1   0   1   5   1   0   6   1   2   0   0
##   GLD  27  15   1  43   0  34 177  34   0  23  32   0   9   1   3   0  26
##   IMS   0   1   0   2   0   0   4  18   0   0   0   0   0   1   0   0   0
##   ISR   0   0  11   3   2   0   0   0  63   0   1   0   6   1  16   6  12
##   LD    0   0   0   0   0   0   0   4   0  41   3   0   7   4   5   0  10
##   LT    3   1   0   5   0   0   3   8   0   9  47   1   0   5   4   0   7
##   MrD  21  26   0   5   0  14   1   2   0   0   0  53   0   0   0   0   0
##   MxD   0   5   0   6   0   4   0   0   3   0   1   0  27   2   2   2   2
##   SB    0   0   0   0   0   0   0   2   0   1   4   0   0  61   9   2   7
##   SD    0   0   2   0   2   0   0   0   2   1   1   1   3   4  22   0   3
##   SSR   0   0   0   7   2   1   0   0  19   1   0   0   7   4   5  86   7
##   TG    0   7   9   5   0   8   0  12   8  13   4   0  22   4   7   4 113
##   WB    0   2   1   5   0   3   7  11   0   0   0   1   2   2   3   0   1
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    1
##   CSR   0
##   DC    0
##   GLD   1
##   IMS   0
##   ISR   0
##   LD    0
##   LT    0
##   MrD   0
##   MxD   0
##   SB    1
##   SD    0
##   SSR   0
##   TG    0
##   WB   97
## [1] "Kappa overall =  0.495109085172924"
## [1] "Tau overall =  0.501032779524023"
## [1] "mean quality =  0.35223497027564"
## [1] "The quality is  0.35223497027564"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.546987394231187"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   72  25   0  13   0  17  18   7   0   0   1  63   1   0   2   0
##      Ant   2  28   0   0   0   0  16   5   0   0   7   0   0   0   0   0
##      CBD   0   0 110   0  20   0   0   5   9   3   0   0   9   2  35   0
##      CD    0   0   4  14   0  14   1   2   1   5   0   0  14   3   2   0
##      CSR   0   0  11   0 123   0   0   0   1   0   0   0   1   0  12   1
##      DC    1   9   7   7   0  36   1   2   1   9   1   1  14   2   4   0
##      GLD  46  41   2  86   0  73 336  84   0  33  52   0  23  10   5   0
##      IMS   2   1   1   0   0   0   4  30   0   0   2   1   4   6   0   0
##      ISR   0   0  21   7  23   1   0   0 104   3   0   0  28   0  39  16
##      LD    0   1   0   4   0   0   0   7   0  83  11   0   2   5   6   0
##      LT    4  10   0  14   0   0   3  16   0  23  99   3   0  21   6   0
##      MrD  48  52   0   4   0  21   4   6   0   0   0 111   1   0   0   0
##      MxD   0   7   3  10   0  11   0   0  18   1   2   0  46   3   5  16
##      SB    1   0   8   0   0   1   2   1   6   4   8   0   0 108  16   1
##      SD    0   0   3   1   6   1   1   0   6   2   3   0   2   5  32   1
##      SSR   0   0   5  13   2   4   0   0  40   0   1   0  16   9  13 158
##      TG    0  18  24  20   1  21   0  21  13  34  12   0  37  22  10   6
##      WB   10   5   0   9   0   1  15  11   0   0   1   3   0   2   6   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD  10   0
##      CD    4   5
##      CSR   0   0
##      DC    2   0
##      GLD  54   6
##      IMS   3   0
##      ISR  28   0
##      LD   14   0
##      LT   20   2
##      MrD   0   0
##      MxD  13   0
##      SB   12   0
##      SD   11   1
##      SSR  13   0
##      TG  214   0
##      WB    2 187
## [1] "classification error rate with altdata:  0.518584521384929"
## [1] "Kappa overall =  0.444154688984576"
## [1] "Tau overall =  0.450910506768899"
## [1] "mean quality =  0.308337012539322"
## [1] "The quality is  0.308337012539322"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.503198571947649"

SVM n=200

##  [1] "Prediction error at end is:  0.6967893551158"  
##  [2] "Prediction error at end is:  0.624486475097648"
##  [3] "Prediction error at end is:  0.57814816615614" 
##  [4] "Prediction error at end is:  0.550914571887003"
##  [5] "Prediction error at end is:  0.511965122121197"
##  [6] "Prediction error at end is:  0.488030339864832"
##  [7] "Prediction error at end is:  0.491592032544043"
##  [8] "Prediction error at end is:  0.489044261843406"
##  [9] "Prediction error at end is:  0.483699453817604"
## [10] "Prediction error at end is:  0.483446621610671"
##                                     k 1
## 1                     Maximum_Height_hr
## 2                     Normalized_Height
## 3                           ValleyDepth
## 4  Vertical_Distance_to_Channel_Network
## 5                       Valley_Depth_hr
## 6                          Slope_Height
## 7                           SlopeHeight
## 8                     Mid_Slope_Positon
## 9               Relative_Slope_Position
## 10               Standardized_Height_hr
##                                     k 2
## 1                     Maximum_Height_hr
## 2                     Normalized_Height
## 3                           ValleyDepth
## 4  Vertical_Distance_to_Channel_Network
## 5                       Valley_Depth_hr
## 6                          Slope_Height
## 7                           SlopeHeight
## 8                   Standardized_Height
## 9                     Mid_Slope_Positon
## 10                   StandardizedHeight
##                                     k 3
## 1                     Maximum_Height_hr
## 2                     Normalized_Height
## 3                          Slope_Height
## 4  Vertical_Distance_to_Channel_Network
## 5                           ValleyDepth
## 6                       Valley_Depth_hr
## 7                    StandardizedHeight
## 8                           SlopeHeight
## 9               Relative_Slope_Position
## 10                    Mid_Slope_Positon
##                                     k 4
## 1                     Maximum_Height_hr
## 2                     Normalized_Height
## 3                           ValleyDepth
## 4                          Slope_Height
## 5  Vertical_Distance_to_Channel_Network
## 6                       Valley_Depth_hr
## 7                           SlopeHeight
## 8               Relative_Slope_Position
## 9                      NormalizedHeight
## 10                   StandardizedHeight
##                                     k 5
## 1                     Maximum_Height_hr
## 2                     Normalized_Height
## 3                           ValleyDepth
## 4                       Valley_Depth_hr
## 5  Vertical_Distance_to_Channel_Network
## 6                          Slope_Height
## 7                           SlopeHeight
## 8               Relative_Slope_Position
## 9                    StandardizedHeight
## 10                  Standardized_Height

##                               allchosen Freq
## 1                     Maximum_Height_hr    5
## 4                     Normalized_Height    5
## 6                           SlopeHeight    5
## 7                          Slope_Height    5
## 11                          ValleyDepth    5
## 12                      Valley_Depth_hr    5
## 13 Vertical_Distance_to_Channel_Network    5
## 5               Relative_Slope_Position    4
## 8                    StandardizedHeight    4
## 2                     Mid_Slope_Positon    3
## 9                   Standardized_Height    2
## [1] "10fold cv-error:  0.484725050916497  for predictors Maximum_Height_hr AND ValleyDepth AND Vertical_Distance_to_Channel_Network AND SlopeHeight AND Valley_Depth_hr AND Normalized_Height"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   40   0   0   5   0   0   6   8   0   0   0   7   0   1   0   0   1
##   Ant   1  38   0   0   0   0   9   6   0   0   6   1   0   1   1   0   1
##   CBD   0   0 127   0  22   0   0   4   7   1   0   0   9   1  24   1   9
##   CD    0   5   3  26   0  12   2   1   1   5   1   0  18   1   1   0   4
##   CSR   0   0   1   0 133   1   0   0   2   0   0   0   0   0  14   1   0
##   DC    7   5   5  25   0  96   8  11   2  20   2   0  18   3   2   0  21
##   GLD  40  44   3  73   0  38 337  65   0  18  32   0  19  10   8   0  31
##   IMS   4   3   1   2   0   0   8  46   0   0   3   0   3   4   0   0   1
##   ISR   0   0  20   3  13   1   0   0 127   2   0   0  23   4  32  18  31
##   LD    0   1   4   4   0   0   0  10   1 111  14   0   3   8   6   0  17
##   LT    8   3   0  13   0   0   3  16   0  15 123   3   0  19   4   0  22
##   MrD  83  73   0  10   0  36  11   5   0   0   0 167   1   0   0   0   0
##   MxD   0   8   2  12   0   7   0   0  11   0   1   0  53   2   2   5   7
##   SB    0   0   4   0   0   1   2   2   2   4   5   0   0 116  13   0  14
##   SD    0   0   4   0   5   1   1   0  10   3   1   1   4   3  61   1  10
##   SSR   0   0   5  15   2   2   0   0  30   0   1   0  15   7  11 163  11
##   TG    0  12  20   6   0   5   0  13   6  21   9   0  32  17   8  10 218
##   WB    3   5   0   8   0   1  14  10   0   0   2   3   0   1   6   0   2
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    1
##   CSR   0
##   DC    0
##   GLD   5
##   IMS   0
##   ISR   0
##   LD    0
##   LT    2
##   MrD   0
##   MxD   0
##   SB    0
##   SD    1
##   SSR   0
##   TG    1
##   WB  191
## [1] "Kappa overall =  0.522474583639682"
## [1] "Tau overall =  0.526925841619744"
## [1] "mean quality =  0.374567214521099"
## [1] "The quality is  0.374567214521099"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.56356126374158"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    7   0   0   1   0   1   1   4   0   0   1   3   0   0   0   0
##      Ant   0  19   0   0   0   1   3   0   0   1   2   0   0   3   1   0
##      CBD   0   0  62   0  10   0   0   4   6   1   0   0   1   4  14   2
##      CD    0   3   1  13   0   2   1   0   1   4   2   0  10   2   1   0
##      CSR   0   0   3   0  68   0   0   0   0   0   0   0   2   0   5   0
##      DC    6   5   3  17   0  37   9   5   1  12   1   0  10   2   1   0
##      GLD  24  18   2  31   0  22 165  28   0  10  24   0   7   1   4   0
##      IMS   1   1   0   2   0   1   7  25   0   0   4   0   3   1   0   0
##      ISR   0   0  14   3   3   1   0   0  59   0   0   0  12   0  17   9
##      LD    0   0   2   1   0   0   0   3   0  55   4   0   6   5   4   0
##      LT    3   0   0   5   0   0   5   8   0   6  53   1   0   8   4   0
##      MrD  50  42   0   7   0  24   2   3   0   0   0  78   0   0   0   0
##      MxD   0   4   0   5   1   2   0   0   4   0   0   0  17   4   3   3
##      SB    0   0   1   0   0   0   0   3   0   0   3   0   0  56   9   1
##      SD    0   0   5   0   8   0   0   0  10   3   3   1   3   3  23   0
##      SSR   0   0   0   7   1   1   0   0  17   1   0   0   8   2   4  79
##      TG    0   5   7   4   0   6   0   5   2   7   3   0  19   7   7   6
##      WB    0   2   1   5   0   2   7   9   0   0   0   1   2   2   3   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD  11   0
##      CD    1   1
##      CSR   1   0
##      DC   11   0
##      GLD  12   1
##      IMS   1   0
##      ISR  12   0
##      LD   11   0
##      LT    7   0
##      MrD   0   0
##      MxD   6   0
##      SB    9   1
##      SD    3   0
##      SSR   6   0
##      TG  108   0
##      WB    2  97
## [1] "classification error rate with altdata:  0.480407124681934"
## [1] "Kappa overall =  0.486902453212378"
## [1] "Tau overall =  0.491333632689717"
## [1] "mean quality =  0.339178536857587"
## [1] "The quality is  0.339178536857587"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.536301366540095"

RandomForest n=100

importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=6,altdata=twohundred,legend=geolegendeng)
## [1] "OBB error with all predictors of  heights is  0.313994910941476"
##                                      MeanDecreaseGini
## Maximum_Height_hr                           262.57103
## Vertical_Distance_to_Channel_Network        160.76117
## Relative_Slope_Position                     157.89584
## ValleyDepth                                 148.66818
## Slope_Height                                133.94411
## Standardized_Height                         131.22079
## Normalized_Height                           113.14580
## SlopeHeight                                  93.60946
## Standardized_Height_hr                       89.71376
## StandardizedHeight                           89.69888
##                                                                parameters
## Maximum_Height_hr                                       Maximum_Height_hr
## Vertical_Distance_to_Channel_Network Vertical_Distance_to_Channel_Network
## Relative_Slope_Position                           Relative_Slope_Position
## ValleyDepth                                                   ValleyDepth
## Slope_Height                                                 Slope_Height
## Standardized_Height                                   Standardized_Height
## Normalized_Height                                       Normalized_Height
## SlopeHeight                                                   SlopeHeight
## Standardized_Height_hr                             Standardized_Height_hr
## StandardizedHeight                                     StandardizedHeight
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   92   4   0   4   0   5   2   8   0   0   4   1   2   2   0   0
##      Ant  13 149   0   4   0   8  21   6   0   1   3  11   2   3   5   0
##      CBD   0   0 135   0  13   0   0   0   6   7   0   0  12   1  22   0
##      CD    9   2   2  93   0  17  14   2   0   1   3   0  12   5   2   5
##      CSR   0   0  11   0 139   0   0   0   5   0   0   0   0   0  16   0
##      DC    9   4   0  12   0 122  12   5   0   2   3   2  10   1   2   0
##      GLD  14  23   1  24   0  13 327  20   0   2  10   0   8   5   4   0
##      IMS   5   1   2  13   0   2  12 136   0   0   1   0   9   5   1   0
##      ISR   0   0   5   3  10   2   0   0 124   1   2   0  18   2  18  18
##      LD    3   0   9   7   0   4   1  11   0 154  14   0   4  10   4   0
##      LT   10  10   0  13   0   0   5   6   0   3 124   2   0   9   1   1
##      MrD  24   0   0   0   0   0   0   0   0   0   0 164   0   0   1   0
##      MxD   0   0   8   6   1  13   0   2  22   2   3   0  99   4   7   7
##      SB    0   2   9   1   0   1   0   0   7   5  11   0   0 122  17   0
##      SD    0   2   8   1   6   1   0   0   3   6   2   0   7   9  76   0
##      SSR   0   0   0  14   5   1   0   0  25   0   5   0   8   2  10 167
##      TG    6   0   9   5   1  12   4   1   7  16  14   0   7  18   7   1
##      WB    1   0   0   2   0   0   3   0   0   0   1   2   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   2   0
##      CBD   6   0
##      CD    8   0
##      CSR   0   0
##      DC    5   0
##      GLD   4   2
##      IMS   3   0
##      ISR  23   0
##      LD   13   2
##      LT   15   2
##      MrD   0   0
##      MxD  11   0
##      SB   12   0
##      SD   14   0
##      SSR  10   0
##      TG  274   1
##      WB    0 194
## [1] "classification error rate with altdata:  0.314918533604888"
evaluateforwardCV_anyerror(mypath=paste(base,"ranfor_fw_5fold_10p_geomorphologie_beschreibung_heights_100pg",sep=""),kk = 1:5,endround = 10,error = "cverror",geheim="geheimerprederror",yrange=c(0,1))

##  [1] "Prediction error at end is:  0.691094147582697"
##  [2] "Prediction error at end is:  0.526717557251908"
##  [3] "Prediction error at end is:  0.419338422391857"
##  [4] "Prediction error at end is:  0.372519083969466"
##  [5] "Prediction error at end is:  0.334860050890585"
##  [6] "Prediction error at end is:  0.310941475826972"
##  [7] "Prediction error at end is:  0.31501272264631" 
##  [8] "Prediction error at end is:  0.318575063613232"
##  [9] "Prediction error at end is:  0.31704834605598" 
## [10] "Prediction error at end is:  0.319083969465649"
##                                     k 1
## 1  Vertical_Distance_to_Channel_Network
## 2                     Maximum_Height_hr
## 3               Relative_Slope_Position
## 4                           ValleyDepth
## 5                     Normalized_Height
## 6                          Slope_Height
## 7                       Valley_Depth_hr
## 8                      NormalizedHeight
## 9                       Slope_Height_hr
## 10                    Mid_Slope_Positon
##                                     k 2
## 1                   Standardized_Height
## 2                     Maximum_Height_hr
## 3  Vertical_Distance_to_Channel_Network
## 4                          Slope_Height
## 5               Relative_Slope_Position
## 6                           ValleyDepth
## 7                      NormalizedHeight
## 8                       Valley_Depth_hr
## 9                           SlopeHeight
## 10                    Mid_Slope_Positon
##                                     k 3
## 1  Vertical_Distance_to_Channel_Network
## 2                     Maximum_Height_hr
## 3               Relative_Slope_Position
## 4                   Standardized_Height
## 5                           ValleyDepth
## 6                          Slope_Height
## 7                           SlopeHeight
## 8                       Valley_Depth_hr
## 9                       Slope_Height_hr
## 10                     NormalizedHeight
##                                     k 4
## 1  Vertical_Distance_to_Channel_Network
## 2                     Maximum_Height_hr
## 3               Relative_Slope_Position
## 4                   Standardized_Height
## 5                          Slope_Height
## 6                           ValleyDepth
## 7                       Valley_Depth_hr
## 8                           SlopeHeight
## 9                    StandardizedHeight
## 10                    Normalized_Height
##                                     k 5
## 1  Vertical_Distance_to_Channel_Network
## 2                     Maximum_Height_hr
## 3               Relative_Slope_Position
## 4                     Normalized_Height
## 5                          Slope_Height
## 6                           ValleyDepth
## 7                      NormalizedHeight
## 8                       Valley_Depth_hr
## 9                       Slope_Height_hr
## 10                          SlopeHeight

##                               allchosen Freq
## 1                     Maximum_Height_hr    5
## 5               Relative_Slope_Position    5
## 7                          Slope_Height    5
## 11                          ValleyDepth    5
## 12                      Valley_Depth_hr    5
## 13 Vertical_Distance_to_Channel_Network    5
## 3                      NormalizedHeight    4
## 6                           SlopeHeight    4
## 4                     Normalized_Height    3
## 8                       Slope_Height_hr    3
## 10                  Standardized_Height    3
## 2                     Mid_Slope_Positon    2

with same predictors as svm

predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Maximum_Height_hr","ValleyDepth","Vertical_Distance_to_Channel_Network","SlopeHeight","Valley_Depth_hr","Normalized_Height"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.32824427480916  for predictors Maximum_Height_hr AND ValleyDepth AND Vertical_Distance_to_Channel_Network AND SlopeHeight AND Valley_Depth_hr AND Normalized_Height"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   95   4   0   4   0   6   4   4   0   1   4   0   3   2   0   0
##      Ant   9 150   1   5   0   6  17   7   0   0   5  12   4   4   3   0
##      CBD   0   0 133   0  15   1   0   0  10   4   2   0   7   5  30   0
##      CD   11   4   1  87   0  14  10   3   2   2   3   0  11   4   3   9
##      CSR   0   0  11   0 138   0   0   0   4   0   0   0   0   0  21   1
##      DC   13   0   2  11   0 111  14   6   1   5   2   1   6   1   2   0
##      GLD  13  20   2  33   0  20 324  29   0   1  13   5   9   7   3   0
##      IMS   5   2   1  11   0   4  18 124   0   0   3   0   6   1   2   0
##      ISR   0   0  11   4  10   1   0   0 116   6   0   0  24   1  23  13
##      LD    1   1   7   6   0   7   1  13   0 146  12   0   5   8   1   0
##      LT    6   3   0  10   0   0   3   2   0   2 123   0   1  12   2   0
##      MrD  22   3   0   0   0   1   0   0   0   0   0 163   1   0   1   0
##      MxD   0   1   1   9   0  16   1   2  24   4   4   0  91   5   9   6
##      SB    1   3   9   1   0   0   1   0   5   5  10   1   2 119  19   0
##      SD    1   6   5   1   5   0   0   0   5   6   0   0   3   7  56   2
##      SSR   0   0   4   8   6   0   0   0  24   0   3   0  16   5  12 167
##      TG    8   0  11   9   1  14   6   7   8  18  15   0   9  17   6   1
##      WB    1   0   0   3   0   0   2   0   0   0   1   0   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   1   0
##      CBD   8   0
##      CD    8   0
##      CSR   0   0
##      DC    8   0
##      GLD   3   2
##      IMS   6   0
##      ISR  25   0
##      LD   15   0
##      LT   15   2
##      MrD   0   0
##      MxD  12   0
##      SB   13   0
##      SD    8   1
##      SSR   8   0
##      TG  269   1
##      WB    1 195
## [1] "classification error rate with altdata:  0.336303462321792"
## [1] "Kappa overall =  0.641616087330147"
## [1] "Tau overall =  0.643913981071043"
## [1] "mean quality =  0.502378933165069"
## [1] "The quality is  0.502378933165069"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.660897909166221"

with own predictors:

predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("Vertical_Distance_to_Channel_Network","Maximum_Height_hr","Relative_Slope_Position","Standardized_Height","Slope_Height","ValleyDepth"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.304834605597964  for predictors Vertical_Distance_to_Channel_Network AND Maximum_Height_hr AND Relative_Slope_Position AND Standardized_Height AND Slope_Height AND ValleyDepth"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   89   7   0   5   0   8   4   5   0   1   5   5   6   1   0   0
##      Ant   9 138   1   6   0   6  19   1   0   1   2  12   2   5   1   0
##      CBD   0   0 135   0  15   0   0   0   6   6   0   0  11   4  25   0
##      CD   10   4   1  87   0   9   9   3   0   0   6   0   9   4   5   2
##      CSR   0   0   5   0 139   0   0   0   8   0   0   0   3   0  15   0
##      DC   11   3   1  13   0 112  13   2   0   3   5   3   5   5   3   0
##      GLD   9  20   4  26   0  15 328  20   0   1   7   4   7   4   5   0
##      IMS   5   5   1  12   0   3   8 148   0   0   1   0   5   3   1   0
##      ISR   0   0   8   0   7   1   0   0 129   1   3   0  15   0  18  13
##      LD    1   0   9   8   1  16   0  11   1 156   6   0   1  12   3   0
##      LT   12   7   0  13   0   1   5   6   0   2 129   0   1  11   4   1
##      MrD  32   2   0   0   0   3   0   0   0   0   0 158   0   0   2   0
##      MxD   0   0   8   6   0   7   1   1  19   1   6   0 101   4   6  11
##      SB    1   3   5   1   0   3   3   0   2   3   9   0   1 116  21   2
##      SD    1   6   7   4   9   0   3   0   6   8   2   0   8  11  72   1
##      SSR   0   0   1  12   4   1   0   0  19   0   4   0  11   2   7 169
##      TG    6   2  13   7   0  16   6   0   9  17  15   0  12  15   5   0
##      WB    0   0   0   2   0   0   2   0   0   0   0   0   0   1   0   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   1   0
##      CBD   7   0
##      CD    6   0
##      CSR   1   0
##      DC    9   0
##      GLD   5   0
##      IMS   4   1
##      ISR  20   0
##      LD   13   0
##      LT   15   4
##      MrD   0   2
##      MxD  15   0
##      SB   12   0
##      SD   13   1
##      SSR   6   0
##      TG  270   1
##      WB    2 192
## [1] "classification error rate with altdata:  0.320773930753564"
## [1] "Kappa overall =  0.658342117634389"
## [1] "Tau overall =  0.660357014496226"
## [1] "mean quality =  0.518108080063575"
## [1] "The quality is  0.518108080063575"
## [1] "#########  Cramer's V =  0.672932773473784"

RandomForest n=200

##  [1] "Prediction error at end is:  0.629581368211212"
##  [2] "Prediction error at end is:  0.495926808317531"
##  [3] "Prediction error at end is:  0.415476896646732"
##  [4] "Prediction error at end is:  0.357433104811916"
##  [5] "Prediction error at end is:  0.32714250984587" 
##  [6] "Prediction error at end is:  0.289466297142672"
##  [7] "Prediction error at end is:  0.269100662874184"
##  [8] "Prediction error at end is:  0.266295521952643"
##  [9] "Prediction error at end is:  0.269349929498712"
## [10] "Prediction error at end is:  0.267058880731269"
##                                     k 1
## 1                   Standardized_Height
## 2                     Normalized_Height
## 3  Vertical_Distance_to_Channel_Network
## 4                          Slope_Height
## 5               Relative_Slope_Position
## 6                     Maximum_Height_hr
## 7                           ValleyDepth
## 8                Standardized_Height_hr
## 9                           SlopeHeight
## 10                   StandardizedHeight
##                                     k 2
## 1                   Standardized_Height
## 2                     Normalized_Height
## 3  Vertical_Distance_to_Channel_Network
## 4               Relative_Slope_Position
## 5                     Maximum_Height_hr
## 6                          Slope_Height
## 7                           ValleyDepth
## 8                    StandardizedHeight
## 9                   MidSlope_Positon_hr
## 10                      Valley_Depth_hr
##                                     k 3
## 1                   Standardized_Height
## 2                     Maximum_Height_hr
## 3               Relative_Slope_Position
## 4  Vertical_Distance_to_Channel_Network
## 5                          Slope_Height
## 6                           ValleyDepth
## 7                     Normalized_Height
## 8                Standardized_Height_hr
## 9                       Valley_Depth_hr
## 10                          SlopeHeight
##                                     k 4
## 1                   Standardized_Height
## 2                     Normalized_Height
## 3  Vertical_Distance_to_Channel_Network
## 4                          Slope_Height
## 5               Relative_Slope_Position
## 6                     Maximum_Height_hr
## 7                           ValleyDepth
## 8                           SlopeHeight
## 9                       Valley_Depth_hr
## 10                 Normalized_Height_hr
##                                     k 5
## 1                   Standardized_Height
## 2                     Normalized_Height
## 3  Vertical_Distance_to_Channel_Network
## 4                          Slope_Height
## 5               Relative_Slope_Position
## 6                     Maximum_Height_hr
## 7                           ValleyDepth
## 8                       Valley_Depth_hr
## 9                     Mid_Slope_Positon
## 10                     NormalizedHeight

##                               allchosen Freq
## 1                     Maximum_Height_hr    5
## 5                     Normalized_Height    5
## 7               Relative_Slope_Position    5
## 9                          Slope_Height    5
## 11                  Standardized_Height    5
## 13                          ValleyDepth    5
## 15 Vertical_Distance_to_Channel_Network    5
## 14                      Valley_Depth_hr    4
## 8                           SlopeHeight    3
## 10                   StandardizedHeight    2
## 12               Standardized_Height_hr    2
predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("Vertical_Distance_to_Channel_Network","Maximum_Height_hr","Relative_Slope_Position","Standardized_Height","Slope_Height","ValleyDepth"),altdata=onehundred,legend=geolegendeng)
## [1] "OOB-error:  0.256109979633401  for predictors Vertical_Distance_to_Channel_Network AND Maximum_Height_hr AND Relative_Slope_Position AND Standardized_Height AND Slope_Height AND ValleyDepth"
## [1] "Kappa overall =  1"
## [1] "Tau overall =  1"
## [1] "mean quality =  1"
## [1] "The quality is  1"
## [1] "#########  Cramer's V =  1"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   43   1   0   5   0   8   0   0   0   0   2  14   0   1   0   0
##      Ant   8  80   0   4   0   5   1   3   0   0   1   1   1   2   2   0
##      CBD   0   0  75   0   4   0   0   0   5   1   0   0   2   3   9   1
##      CD    5   0   0  50   0   6   3   0   0   1   4   0   2   1   1   0
##      CSR   0   0   3   0  72   0   0   0   2   0   0   0   2   0   9   0
##      DC    6   2   1   7   0  63   3   2   0   5   1   1   2   1   0   0
##      GLD   8   5   2  10   0   8 182   7   0   1   5   0   5   1   3   0
##      IMS   0   0   0   5   0   0   3  84   0   0   1   0   4   1   1   0
##      ISR   0   0   2   2   1   0   0   0  75   0   2   0   8   2   5   4
##      LD    2   0   4   0   0   3   0   0   0  87   4   0   0   5   6   0
##      LT    5   0   0   4   0   1   3   1   0   0  70   0   2   3   2   1
##      MrD  12  11   0   0   0   1   2   0   0   0   0  67   0   1   0   0
##      MxD   0   0   4   5   0   3   0   0   3   0   2   0  62   2   5   0
##      SB    0   0   5   1   0   0   1   0   0   2   4   0   0  57  13   0
##      SD    0   0   3   0  13   0   1   0   4   1   1   0   5   8  38   0
##      SSR   0   0   0   4   1   0   0   0   7   0   0   0   3   4   2  94
##      TG    2   0   2   4   0   2   1   0   4   2   3   0   2   8   3   0
##      WB    0   0   0   0   0   0   0   0   0   0   0   1   0   0   1   0
##         
## altpreds  TG  WB
##      AD    1   0
##      Ant   1   0
##      CBD   1   0
##      CD    4   0
##      CSR   2   0
##      DC    8   0
##      GLD   2   0
##      IMS   1   0
##      ISR  15   0
##      LD    9   0
##      LT    7   0
##      MrD   0   0
##      MxD   3   0
##      SB    8   2
##      SD    2   0
##      SSR   3   0
##      TG  134   0
##      WB    0  98
## [1] "classification error rate with altdata:  0.27175572519084"
## [1] "Kappa overall =  0.710708986435185"
## [1] "Tau overall =  0.712258643915581"
## [1] "mean quality =  0.573752690176201"
## [1] "The quality is  0.573752690176201"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  0.722370767199137"

ALLGEOMS

SVM n=100

##  [1] "Prediction error at end is:  0.81323155216285" 
##  [2] "Prediction error at end is:  0.762849872773537"
##  [3] "Prediction error at end is:  0.759287531806616"
##  [4] "Prediction error at end is:  0.766412213740458"
##  [5] "Prediction error at end is:  0.767430025445293"
##  [6] "Prediction error at end is:  0.773027989821883"
##  [7] "Prediction error at end is:  0.768956743002545"
##  [8] "Prediction error at end is:  0.769465648854962"
##  [9] "Prediction error at end is:  0.772519083969466"
## [10] "Prediction error at end is:  0.762849872773537"
##                            k 1                         k 2
## 1  geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl2_L1500m
## 2  geom_DTM_50m_avg_fl10_L150m geom_DTM_50m_avg_fl10_L250m
## 3             geom_10m_fl10_L8  geom_DTM_50m_avg_fl1_L150m
## 4  geom_DTM_50m_avg_fl2_L1300m geom_DTM_50m_avg_fl3_L1500m
## 5              geom_10m_fl2_L4 geom_DTM_50m_avg_fl1_L1000m
## 6   geom_DTM_50m_avg_fl4_L200m  geom_DTM_50m_avg_fl4_L800m
## 7             geom_10m_fl1_L47            geom_10m_fl1_L90
## 8             geom_10m_fl4_L30  geom_DTM_50m_avg_fl3_L900m
## 9   geom_DTM_50m_avg_fl1_L200m           geom_10m_fl8_L110
## 10           geom_10m_fl1_L130           geom_10m_fl3_L120
##                            k 3                         k 4
## 1  geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl1_L1500m
## 2  geom_DTM_50m_avg_fl10_L150m geom_DTM_50m_avg_fl10_L150m
## 3             geom_10m_fl10_L3             geom_10m_fl2_L4
## 4            geom_10m_fl2_L110           geom_10m_fl1_L140
## 5              geom_10m_fl3_L6 geom_DTM_50m_avg_fl8_L1400m
## 6            geom_10m_fl10_L70  geom_DTM_50m_avg_fl2_L400m
## 7   geom_DTM_50m_avg_fl4_L500m            geom_10m_fl10_L7
## 8             geom_10m_fl3_L46            geom_10m_fl4_L12
## 9  geom_DTM_50m_avg_fl4_L1500m   geom_dtm_10m_hyd_fl5_L100
## 10  geom_DTM_50m_avg_fl1_L300m  geom_DTM_50m_avg_fl1_L150m
##                            k 5
## 1  geom_DTM_50m_avg_fl1_L1500m
## 2  geom_DTM_50m_avg_fl10_L150m
## 3             geom_10m_fl10_L3
## 4            geom_10m_fl2_L130
## 5             geom_10m_fl1_L70
## 6   geom_DTM_50m_avg_fl1_L150m
## 7             geom_10m_fl10_L7
## 8   geom_DTM_50m_avg_fl4_L600m
## 9            geom_10m_fl10_L11
## 10  geom_DTM_50m_avg_fl1_L250m

##                      allchosen Freq
## 21 geom_DTM_50m_avg_fl10_L150m    4
## 24 geom_DTM_50m_avg_fl1_L1500m    4
## 25  geom_DTM_50m_avg_fl1_L150m    3
## 2             geom_10m_fl10_L3    2
## 3             geom_10m_fl10_L7    2
## 13             geom_10m_fl2_L4    2
## [1] "10fold cv-error:  0.750127226463104  for predictors geom_DTM_50m_avg_fl1_L1500m AND geom_DTM_50m_avg_fl10_L150m"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   15   0   0   0   0   0   0   0   0   0   0   6   0   0   0   0   0
##   Ant   0   5   0   0   0   0   0   0   0   0   0   0   0   2   1   0   2
##   CBD   2   0   9   2   5   0  11   2   6   3   1   0   9   2  10   2   3
##   CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   DC    9  20   0  30   0  68  16   7   0  28  30   2  10   2   0   4  24
##   GLD  11  14   1  25   8   5  64  18  13  12  22   0  22  17   5  12  15
##   IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   ISR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   LD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   LT    1   2   0   8   0   5  23   2   0  10  23   0   4   0   1   9  10
##   MrD  25  12   0   0   0   0   0   0   0   0   0  62   0   0   0   0   0
##   MxD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   SB    0  11   0   0   1   0  16   8   0   0   6   0   0  21   1   3   3
##   SD    0   0   5   2   6   0   4   2   7   2   0   0   6   1   7   7   1
##   SSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   3   0
##   TG   15  19  86  15  71   7  48  58  74  43  15   0  49  55  75  60 143
##   WB   13  16   0  19   0  15  18   0   0   2   3  14   0   0   0   0   0
##      
## preds  WB
##   AD    0
##   Ant   0
##   CBD   0
##   CD    0
##   CSR   0
##   DC    2
##   GLD   2
##   IMS   0
##   ISR   0
##   LD    0
##   LT    0
##   MrD   5
##   MxD   0
##   SB    0
##   SD    0
##   SSR   0
##   TG    0
##   WB   91
## [1] "Kappa overall =  0.196490341943895"
## [1] "Tau overall =  0.216524472384374"
## [1] "mean quality =  0.117602056036468"
## [1] "The quality is  0.117602056036468"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   30   0   0   0   0   0   0   0   0   0   0  19   0   0   0   0
##      Ant   0   5   0   0   0   0   0   1   0   0   2   0   0  10   2   0
##      CBD   3   2  12   3  10   2  27   7  16   4   3   0  15   6  21   8
##      CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      DC   15  48   0  59   0 114  22  10   0  70  41   5  21   2   3   3
##      GLD  22  31   8  36  11  18  92  38  24  18  57   1  50  25  13  35
##      IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      ISR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      LD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      LT    6   3   0  15   0  15  37   4   0  13  24   0   7   4   1  12
##      MrD  49  20   0   0   0   2   0   0   0   0   0 116   0   0   0   0
##      MxD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SB    3  22   0   1  10   1  20  15   1   1  17   0   0  36   2   6
##      SD    1   0   6   4  11   0   2  15  13   1   1   0  10   3   2  11
##      SSR   0   1   0   0   0   0   0   0   0   0   1   0   0   3   0   0
##      TG   24  41 173  50 133  20 143 107 145  91  42   0  93 107 148 124
##      WB   33  24   0  34   0  29  58   0   0   2  12  41   2   2   1   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   6   0
##      CBD  11   0
##      CD    0   0
##      CSR   0   0
##      DC   42   9
##      GLD  32   2
##      IMS   0   0
##      ISR   0   0
##      LD    0   0
##      LT   20   0
##      MrD   0   4
##      MxD   0   0
##      SB    1   0
##      SD    2   0
##      SSR   0   0
##      TG  285   1
##      WB    1 185
## [1] "classification error rate with altdata:  0.770621181262729"
## [1] "Kappa overall =  0.162635905524366"
## [1] "Tau overall =  0.184048161015934"
## [1] "mean quality =  0.0971995432521506"
## [1] "The quality is  0.0971995432521506"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

SVM n=200

##  [1] "Prediction error at end is:  0.824595387432943"
##  [2] "Prediction error at end is:  0.784106902643393"
##  [3] "Prediction error at end is:  0.775198132931395"
##  [4] "Prediction error at end is:  0.765271551514562"
##  [5] "Prediction error at end is:  0.755341728659179"
##  [6] "Prediction error at end is:  0.752034164762322"
##  [7] "Prediction error at end is:  0.754578045736698"
##  [8] "Prediction error at end is:  0.752030923323771"
##  [9] "Prediction error at end is:  0.742612275327791"
## [10] "Prediction error at end is:  0.742105314338503"
##                             k 1                          k 2
## 1   geom_DTM_50m_avg_fl1_L1400m  geom_DTM_50m_avg_fl1_L1400m
## 2  geom_DTM_50m_avg_fl10_L1500m geom_DTM_50m_avg_fl10_L1500m
## 3    geom_DTM_50m_avg_fl2_L150m             geom_10m_fl10_L7
## 4   geom_DTM_50m_avg_fl10_L300m   geom_DTM_50m_avg_fl2_L300m
## 5   geom_DTM_50m_avg_fl4_L1500m            geom_10m_fl1_L120
## 6   geom_DTM_50m_avg_fl8_L1500m  geom_DTM_50m_avg_fl1_L1500m
## 7   geom_DTM_50m_avg_fl2_L1500m              geom_10m_fl2_L5
## 8    geom_DTM_50m_avg_fl2_L250m             geom_10m_fl8_L40
## 9              geom_10m_fl8_L16            geom_10m_fl10_L14
## 10             geom_10m_fl1_L12   geom_DTM_50m_avg_fl2_L500m
##                             k 3                         k 4
## 1             geom_10m_fl2_L150 geom_DTM_50m_avg_fl1_L1400m
## 2             geom_10m_fl10_L12 geom_DTM_50m_avg_fl10_L150m
## 3   geom_DTM_50m_avg_fl10_L150m           geom_10m_fl10_L11
## 4               geom_10m_fl1_L5  geom_DTM_50m_avg_fl1_L600m
## 5    geom_DTM_50m_avg_fl2_L300m  geom_DTM_50m_avg_fl1_L150m
## 6  geom_DTM_50m_avg_fl10_L1500m geom_DTM_50m_avg_fl4_L1500m
## 7   geom_DTM_50m_avg_fl10_L500m           geom_10m_fl1_L110
## 8      geom_dtm_10m_hyd_fl5_L31            geom_10m_fl10_L3
## 9   geom_DTM_50m_avg_fl3_L1500m geom_DTM_50m_avg_fl10_L800m
## 10             geom_10m_fl10_L3  geom_DTM_50m_avg_fl4_L250m
##                            k 5
## 1  geom_DTM_50m_avg_fl1_L1400m
## 2  geom_DTM_50m_avg_fl10_L150m
## 3             geom_10m_fl10_L4
## 4   geom_DTM_50m_avg_fl4_L250m
## 5  geom_DTM_50m_avg_fl10_L600m
## 6  geom_DTM_50m_avg_fl3_L1400m
## 7   geom_DTM_50m_avg_fl1_L150m
## 8  geom_DTM_50m_avg_fl2_L1000m
## 9              geom_10m_fl1_L6
## 10           geom_10m_fl10_L14

##                       allchosen Freq
## 23  geom_DTM_50m_avg_fl1_L1400m    4
## 17 geom_DTM_50m_avg_fl10_L1500m    3
## 18  geom_DTM_50m_avg_fl10_L150m    3
## 3             geom_10m_fl10_L14    2
## 4              geom_10m_fl10_L3    2
## 25   geom_DTM_50m_avg_fl1_L150m    2
## 31   geom_DTM_50m_avg_fl2_L300m    2
## 35  geom_DTM_50m_avg_fl4_L1500m    2
## 36   geom_DTM_50m_avg_fl4_L250m    2
## [1] "10fold cv-error:  0.764002036659878  for predictors geom_DTM_50m_avg_fl1_L1500m AND geom_DTM_50m_avg_fl10_L150m"
##      
## preds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR  TG
##   AD   30   0   0   0   0   0   0   0   0   0   0  19   0   0   0   0   0
##   Ant   6  23   0  29   0  25   8   1   0  16   7   5   5   0   0   0  14
##   CBD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   DC    9  25   0  30   0  89  14   9   0  54  34   0  16   2   3   3  28
##   GLD  15  14   1  22   6  18 109  28   4  25  68   0  15  25   5  30  46
##   IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   ISR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   LD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   LT    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   MrD  49  20   0   0   0   2   0   0   0   0   0 116   0   0   0   0   0
##   MxD  14  20  13  33  16  15  22  29  33   7  14   1  52   8  12  28   8
##   SB    3  28   0   1  10   1  20  16   1   1  20   0   0  49   4   6   7
##   SD    3   2  12   3  10   2  27   7  16   4   3   0  15   5  20   8  11
##   SSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##   TG   24  41 173  50 133  20 143 107 145  91  42   0  93 107 148 124 285
##   WB   33  24   0  34   0  29  58   0   0   2  12  41   2   2   1   0   1
##      
## preds  WB
##   AD    0
##   Ant   7
##   CBD   0
##   CD    0
##   CSR   0
##   DC    2
##   GLD   0
##   IMS   0
##   ISR   0
##   LD    0
##   LT    0
##   MrD   4
##   MxD   2
##   SB    0
##   SD    0
##   SSR   0
##   TG    1
##   WB  185
## [1] "Kappa overall =  0.1794447323937"
## [1] "Tau overall =  0.199412962741105"
## [1] "mean quality =  0.107211034953502"
## [1] "The quality is  0.107211034953502"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   15   0   0   0   0   0   0   0   0   0   0   6   0   0   0   0
##      Ant   8  12   0   9   0  19   7   1   0   6   9   1   1   0   0   0
##      CBD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      DC    1   8   0  21   0  49   9   6   0  22  21   1   9   2   0   4
##      GLD   2   9   1  13   4   6  67  15   1  13  40   0   9  13   3  15
##      IMS   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      ISR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      LD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      LT    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      MrD  25  12   0   0   0   0   0   0   0   0   0  62   0   0   0   0
##      MxD  10   7   5  22  10   4  24   7  19  11   5   0  23   5  10  13
##      SB    0  16   0   0   1   0  16   8   0   0   6   0   0  23   2   6
##      SD    2   0   9   2   5   0  11   2   6   3   1   0   9   2  10   2
##      SSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      TG   15  19  86  15  71   7  48  58  74  43  15   0  49  55  75  60
##      WB   13  16   0  19   0  15  18   0   0   2   3  14   0   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant  10   2
##      CBD   0   0
##      CD    0   0
##      CSR   0   0
##      DC   14   0
##      GLD  21   1
##      IMS   0   0
##      ISR   0   0
##      LD    0   0
##      LT    0   0
##      MrD   0   5
##      MxD   5   1
##      SB    5   0
##      SD    3   0
##      SSR   0   0
##      TG  143   0
##      WB    0  91
## [1] "classification error rate with altdata:  0.748091603053435"
## [1] "Kappa overall =  0.188434015222394"
## [1] "Tau overall =  0.207903008531657"
## [1] "mean quality =  0.113132569009515"
## [1] "The quality is  0.113132569009515"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
importance_ranfor_pset_newlegend(modeldata=onehundred,dependent="geomorphologie_beschreibung",pset=7,altdata=twohundred,legend=geolegendeng)
## [1] "OBB error with all predictors of  allgeoms is  0.623409669211196"
##                             MeanDecreaseGini                  parameters
## geom_10m_fl10_L3                   12.049847            geom_10m_fl10_L3
## geom_DTM_50m_avg_fl1_L150m         11.728523  geom_DTM_50m_avg_fl1_L150m
## geom_DTM_50m_avg_fl10_L200m        11.350796 geom_DTM_50m_avg_fl10_L200m
## geom_DTM_50m_avg_fl10_L150m        11.189319 geom_DTM_50m_avg_fl10_L150m
## geom_DTM_50m_avg_fl1_L200m         10.745587  geom_DTM_50m_avg_fl1_L200m
## geom_10m_fl1_L4                    10.559035             geom_10m_fl1_L4
## geom_10m_fl10_L4                   10.169567            geom_10m_fl10_L4
## geom_10m_fl1_L3                     9.850641             geom_10m_fl1_L3
## geom_DTM_50m_avg_fl1_L250m          9.710074  geom_DTM_50m_avg_fl1_L250m
## geom_10m_fl1_L5                     9.260687             geom_10m_fl1_L5
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   57   9   4   5   0   4   3   2   0   1   1  33   2   1   1   3
##      Ant   7  67   2   5   2   3   6   3   1   1   1  14   2   7   1   3
##      CBD   8   9  92   9  49   6  46  17  36  38   8   0  32  33  64  45
##      CD   10  11   0  65   2  23  14   2   2   5  17   5  11   2   2   4
##      CSR   3   6  10   1  36   0  10   9  22   3   1   0   8  18  15  18
##      DC   24  16   0  24   0 104  12   1   0  17  12   3   7   0   1   0
##      GLD  12  19   2  24   1  10 184  18   8  19  41   1  18  24  12   6
##      IMS   5   4   9  10   2   3  15  69  12   9   6   0  17   8   8   7
##      ISR   1   3  22   4  16   1   8  15  36   3   3   0  13  12  18  19
##      LD    6   4   6  10   4   4  15   4   4  51   8   1   7   6   5   4
##      LT    3   3   1   3   0  18  16   4   1  17  60   0   2   4   1   0
##      MrD  32   4   0   0   0   5   0   0   0   0   0 105   0   0   0   0
##      MxD   4   4   6  13  11   6  20  14  17   5   8   0  42   3  10  17
##      SB    6  12   2   5   8   4  13   2   5   2  14   1   1  35   5  10
##      SD    0   2  15   4  18   0   4  13  14   2   5   0   8  11  18   7
##      SSR   0   5   7   2   7   1   3   8   9   4   1   0   4  11  10  36
##      TG    3  17  21  17  19   8  29  16  32  23  14   0  24  22  22  20
##      WB    5   2   0   1   0   1   3   0   0   0   0  19   0   1   0   0
##         
## altpreds  TG  WB
##      AD    1   4
##      Ant   6   0
##      CBD  70   0
##      CD   10   2
##      CSR  14   0
##      DC   12   1
##      GLD  28   7
##      IMS  14   0
##      ISR  26   0
##      LD   14   0
##      LT   18   0
##      MrD   0   2
##      MxD  17   0
##      SB   13   0
##      SD    6   0
##      SSR   8   0
##      TG  143   1
##      WB    0 184
## [1] "classification error rate with altdata:  0.647657841140529"

RandomForest n=100

##  [1] "Prediction error at end is:  0.816793893129771"
##  [2] "Prediction error at end is:  0.772519083969466"
##  [3] "Prediction error at end is:  0.7735368956743"  
##  [4] "Prediction error at end is:  0.734351145038168"
##  [5] "Prediction error at end is:  0.722137404580153"
##  [6] "Prediction error at end is:  0.716539440203562"
##  [7] "Prediction error at end is:  0.700254452926209"
##  [8] "Prediction error at end is:  0.690076335877863"
##  [9] "Prediction error at end is:  0.689058524173028"
## [10] "Prediction error at end is:  0.695674300254453"
##                             k 1                          k 2
## 1   geom_DTM_50m_avg_fl1_L1500m  geom_DTM_50m_avg_fl1_L1500m
## 2  geom_DTM_50m_avg_fl10_L1100m            geom_10m_fl10_L60
## 3    geom_DTM_50m_avg_fl3_L600m geom_DTM_50m_avg_fl10_L1500m
## 4    geom_DTM_50m_avg_fl8_L200m   geom_DTM_50m_avg_fl4_L600m
## 5   geom_DTM_50m_avg_fl4_L1100m  geom_DTM_50m_avg_fl4_L1500m
## 6              geom_10m_fl1_L38   geom_DTM_50m_avg_fl2_L400m
## 7    geom_DTM_50m_avg_fl4_L250m  geom_DTM_50m_avg_fl10_L250m
## 8   geom_DTM_50m_avg_fl10_L500m   geom_DTM_50m_avg_fl3_L200m
## 9   geom_DTM_50m_avg_fl1_L1000m  geom_DTM_50m_avg_fl10_L500m
## 10             geom_10m_fl3_L39  geom_DTM_50m_avg_fl4_L1000m
##                             k 3                         k 4
## 1   geom_DTM_50m_avg_fl1_L1500m geom_DTM_50m_avg_fl2_L1500m
## 2  geom_DTM_50m_avg_fl10_L1100m geom_DTM_50m_avg_fl10_L400m
## 3    geom_DTM_50m_avg_fl2_L600m geom_DTM_50m_avg_fl1_L1200m
## 4              geom_10m_fl2_L22             geom_10m_fl1_L4
## 5   geom_DTM_50m_avg_fl4_L1000m geom_DTM_50m_avg_fl4_L1000m
## 6    geom_DTM_50m_avg_fl8_L400m geom_DTM_50m_avg_fl8_L1200m
## 7   geom_DTM_50m_avg_fl8_L1200m            geom_10m_fl2_L16
## 8               geom_10m_fl1_L4 geom_DTM_50m_avg_fl10_L200m
## 9     geom_dtm_10m_hyd_fl5_L130 geom_DTM_50m_avg_fl10_L600m
## 10   geom_DTM_50m_avg_fl3_L150m  geom_DTM_50m_avg_fl2_L200m
##                             k 5
## 1   geom_DTM_50m_avg_fl1_L1500m
## 2   geom_DTM_50m_avg_fl10_L400m
## 3              geom_10m_fl4_L48
## 4    geom_DTM_50m_avg_fl4_L500m
## 5  geom_DTM_50m_avg_fl10_L1400m
## 6    geom_DTM_50m_avg_fl1_L300m
## 7    geom_DTM_50m_avg_fl8_L200m
## 8   geom_DTM_50m_avg_fl2_L1500m
## 9   geom_DTM_50m_avg_fl4_L1500m
## 10   geom_DTM_50m_avg_fl8_L250m

##                       allchosen Freq
## 19  geom_DTM_50m_avg_fl1_L1500m    4
## 28  geom_DTM_50m_avg_fl4_L1000m    3
## 3               geom_10m_fl1_L4    2
## 9  geom_DTM_50m_avg_fl10_L1100m    2
## 14  geom_DTM_50m_avg_fl10_L400m    2
## 15  geom_DTM_50m_avg_fl10_L500m    2
## 21  geom_DTM_50m_avg_fl2_L1500m    2
## 30  geom_DTM_50m_avg_fl4_L1500m    2
## 34  geom_DTM_50m_avg_fl8_L1200m    2
## 35   geom_DTM_50m_avg_fl8_L200m    2
predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("geom_DTM_50m_avg_fl1_L1500m","geom_DTM_50m_avg_fl10_L400m"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.750636132315522  for predictors geom_DTM_50m_avg_fl1_L1500m AND geom_DTM_50m_avg_fl10_L400m"
## [1] "Kappa overall =  0.226580877711267"
## [1] "Tau overall =  0.242388863942524"
## [1] "mean quality =  0.140203298701791"
## [1] "The quality is  0.140203298701791"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   39  19   0  11   0   9  13   3   0   2   4  20   4   1   2   0
##      Ant   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CBD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CD    1   0   0   0   0   1   0   0   0   0   0   0   0   0   0   0
##      CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      DC   16  36   0  41   0 102  10   6   0  62  39   3   6   1   2   3
##      GLD   8  27   8  32  11  17 119  46  14  21  56   2  29  24  20  15
##      IMS   1   6   0  15   0  17  10   5   0  15   5   0   9   1   5   4
##      ISR   5   5  29  18  24   3  38  32  56  10  17   0  23  36  29  33
##      LD    2   0   0   4   0   0   8   1   0   9   5   0   3   2   0   0
##      LT    6   3   0  11   0  14  17   0   0   9  24   0   6   7   0   9
##      MrD  49  20   0   2   0   2   0   0   0   1   0 116   0   0   0   0
##      MxD   3   0  12   6  18   2   6  12  20   3   2   1  28   4   8  22
##      SB    6  24   0   0   8   0  18   6   0   0   8   0   0  28   0   4
##      SD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SSR   0   2   0   0   0   0   0   0   0   1   2   0   1   4   0   6
##      TG   21  39 150  31 114  12 111  86 109  67  28   0  89  89 126 103
##      WB   29  16   0  31   0  22  51   0   0   0  10  40   0   1   1   0
##         
## altpreds  TG  WB
##      AD    1  13
##      Ant   0   0
##      CBD   0   0
##      CD    0   0
##      CSR   0   0
##      DC   32   7
##      GLD  32   2
##      IMS  13   0
##      ISR  31   1
##      LD    4   0
##      LT   18   0
##      MrD   0   4
##      MxD   6   0
##      SB    1   0
##      SD    0   0
##      SSR   1   0
##      TG  260   0
##      WB    1 174
## [1] "classification error rate with altdata:  0.754073319755601"
## [1] "Kappa overall =  0.184412025186994"
## [1] "Tau overall =  0.201569426141129"
## [1] "mean quality =  0.111793910887642"
## [1] "The quality is  0.111793910887642"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

Für höchste importance:

predict_ranfor_newlegend_full(modeldata =onehundred,dependent="geomorphologie_beschreibung",predictors=c("geom_10m_fl10_L3","geom_DTM_50m_avg_fl10_L200m"),altdata=twohundred,legend=geolegendeng)
## [1] "OOB-error:  0.830534351145038  for predictors geom_10m_fl10_L3 AND geom_DTM_50m_avg_fl10_L200m"
## [1] "Kappa overall =  0.141536576379661"
## [1] "Tau overall =  0.147013920071846"
## [1] "mean quality =  0.0663663073559193"
## [1] "The quality is  0.0663663073559193"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      Ant   0   1   0   0   3   0   3   3   0   0   1   0   1   1   0   3
##      CBD  18  21 150  28 108   8  98  58 116  61  23   0  64  95 122  92
##      CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CSR   1   0   0   2   2   1   0   0   3   0   0   0   2   1   1   1
##      DC    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      GLD  16  39   3  29   6  18  76  38   2  61  46   0  30  31  13  19
##      IMS   3   2   2   2   2   1  16  11   1   4   3   0  10   3   3   6
##      ISR   2   9  28  10  37   1  25  22  57   4  13   0  35  24  31  37
##      LD    1   1   0   4   0   6   5   2   0   7   3   0   1   2   0   1
##      LT    0   1   0   1   0   0   0   0   0   0   0   0   0   1   0   0
##      MrD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      MxD   1   0   0   1   0   3   1   0   2   0   0   0   7   1   1   3
##      SB    4   5   0   0   4   0  11   6   3   0   2   0   2   8   1   1
##      SD    1   2  14   3  12   0   6  21  11   2   1   0  11   4  11  12
##      SSR   1   2   0   0   0   0   1   2   0   0   1   0   0   1   0   2
##      TG    6  33   2  31   1  16  31  27   4   9   7   1  15  10   6  12
##      WB  132  81   0  91   0 147 128   7   0  52 100 181  20  16   4  10
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD 165   0
##      CD    0   0
##      CSR   0   0
##      DC    0   0
##      GLD  59   1
##      IMS   9   1
##      ISR  27   0
##      LD    3   0
##      LT    0   0
##      MrD   0   0
##      MxD   0   0
##      SB    2   0
##      SD    7   0
##      SSR   1   0
##      TG   71   3
##      WB   56 196
## [1] "classification error rate with altdata:  0.847505091649694"
## [1] "Kappa overall =  0.0976050460037231"
## [1] "Tau overall =  0.102641667665029"
## [1] "mean quality =  0.0444539070841878"
## [1] "The quality is  0.0444539070841878"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"

RandomForest n=200

##  [1] "Prediction error at end is:  0.819756243821008"
##  [2] "Prediction error at end is:  0.768330172930747"
##  [3] "Prediction error at end is:  0.748220288163887"
##  [4] "Prediction error at end is:  0.714099609406655"
##  [5] "Prediction error at end is:  0.703153919709567"
##  [6] "Prediction error at end is:  0.692716163433332"
##  [7] "Prediction error at end is:  0.684315651286041"
##  [8] "Prediction error at end is:  0.679731608888025"
##  [9] "Prediction error at end is:  0.670055914814995"
## [10] "Prediction error at end is:  0.666241065784995"
##                             k 1                          k 2
## 1   geom_DTM_50m_avg_fl1_L1300m  geom_DTM_50m_avg_fl1_L1400m
## 2  geom_DTM_50m_avg_fl10_L1400m geom_DTM_50m_avg_fl10_L1500m
## 3    geom_DTM_50m_avg_fl3_L500m   geom_DTM_50m_avg_fl3_L500m
## 4    geom_DTM_50m_avg_fl4_L250m   geom_DTM_50m_avg_fl8_L250m
## 5   geom_DTM_50m_avg_fl2_L1400m   geom_DTM_50m_avg_fl1_L150m
## 6    geom_DTM_50m_avg_fl1_L400m  geom_DTM_50m_avg_fl10_L150m
## 7              geom_10m_fl10_L6  geom_DTM_50m_avg_fl8_L1200m
## 8    geom_DTM_50m_avg_fl1_L200m   geom_DTM_50m_avg_fl4_L800m
## 9   geom_DTM_50m_avg_fl4_L1400m  geom_DTM_50m_avg_fl4_L1500m
## 10   geom_DTM_50m_avg_fl3_L200m   geom_DTM_50m_avg_fl2_L700m
##                             k 3                          k 4
## 1   geom_DTM_50m_avg_fl1_L1500m  geom_DTM_50m_avg_fl1_L1300m
## 2    geom_DTM_50m_avg_fl8_L400m geom_DTM_50m_avg_fl10_L1500m
## 3    geom_DTM_50m_avg_fl1_L200m   geom_DTM_50m_avg_fl2_L900m
## 4    geom_DTM_50m_avg_fl3_L400m   geom_DTM_50m_avg_fl4_L500m
## 5  geom_DTM_50m_avg_fl10_L1300m             geom_10m_fl10_L7
## 6   geom_DTM_50m_avg_fl10_L150m  geom_DTM_50m_avg_fl10_L600m
## 7   geom_DTM_50m_avg_fl4_L1500m   geom_DTM_50m_avg_fl1_L150m
## 8    geom_DTM_50m_avg_fl2_L300m            geom_10m_fl4_L120
## 9   geom_DTM_50m_avg_fl4_L1100m  geom_DTM_50m_avg_fl4_L1500m
## 10   geom_DTM_50m_avg_fl4_L300m  geom_DTM_50m_avg_fl8_L1100m
##                             k 5
## 1   geom_DTM_50m_avg_fl1_L1400m
## 2   geom_DTM_50m_avg_fl10_L600m
## 3    geom_DTM_50m_avg_fl4_L250m
## 4  geom_DTM_50m_avg_fl10_L1300m
## 5    geom_DTM_50m_avg_fl1_L250m
## 6    geom_DTM_50m_avg_fl8_L500m
## 7    geom_DTM_50m_avg_fl8_L250m
## 8    geom_DTM_50m_avg_fl4_L700m
## 9   geom_DTM_50m_avg_fl1_L1000m
## 10   geom_DTM_50m_avg_fl2_L500m

##                       allchosen Freq
## 27  geom_DTM_50m_avg_fl4_L1500m    3
## 4  geom_DTM_50m_avg_fl10_L1300m    2
## 6  geom_DTM_50m_avg_fl10_L1500m    2
## 7   geom_DTM_50m_avg_fl10_L150m    2
## 8   geom_DTM_50m_avg_fl10_L600m    2
## 10  geom_DTM_50m_avg_fl1_L1300m    2
## 11  geom_DTM_50m_avg_fl1_L1400m    2
## 13   geom_DTM_50m_avg_fl1_L150m    2
## 14   geom_DTM_50m_avg_fl1_L200m    2
## 24   geom_DTM_50m_avg_fl3_L500m    2
## 28   geom_DTM_50m_avg_fl4_L250m    2
## 35   geom_DTM_50m_avg_fl8_L250m    2
predict_ranfor_newlegend_full(modeldata =twohundred,dependent="geomorphologie_beschreibung",predictors=c("geom_DTM_50m_avg_fl1_L1300m","geom_DTM_50m_avg_fl10_L1500m"),altdata=onehundred,legend=geolegendeng)
## [1] "OOB-error:  0.75  for predictors geom_DTM_50m_avg_fl1_L1300m AND geom_DTM_50m_avg_fl10_L1500m"
## [1] "Kappa overall =  0.19829476701871"
## [1] "Tau overall =  0.213699532766263"
## [1] "mean quality =  0.117860006104573"
## [1] "The quality is  0.117860006104573"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"
##         
## altpreds  AD Ant CBD  CD CSR  DC GLD IMS ISR  LD  LT MrD MxD  SB  SD SSR
##      AD   20   2   0   0   0   0   0   0   0   0   0  17   0   0   0   0
##      Ant   0   0   0   0   1   0   2   0   0   0   0   0   0   2   0   0
##      CBD   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      CSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      DC    6   8   1  24   0  37  17  12   0  25   5   0   7   2   3   0
##      GLD   4   6  11  13   2   0  74  26  20  14  30   1  11  13  13  17
##      IMS   2   4   0   1   0   2   3  16   0   0   2   0   2   2   2   2
##      ISR   2   4   1   9   2   0   7   2   9   0   1   0   6   3   2   6
##      LD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      LT    5  14   0  13   0  35  22   0   0  20  45   1   2   0   0   0
##      MrD  21  10   0   0   0   1   0   0   0   0   0  53   0   0   0   0
##      MxD   1   0   9   2  13   0   7   3  14   5   1   0  19   4  13   9
##      SB    0  18  12   2  21   1  17  17  13   1   1   0   7  44  11  25
##      SD    0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      SSR   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0   0
##      TG   11  18  67  14  52  12  27  20  44  33  12   0  44  30  56  41
##      WB   19  15   0  23   0  12  24   1   0   2   3  12   2   0   0   0
##         
## altpreds  TG  WB
##      AD    0   0
##      Ant   0   0
##      CBD   0   0
##      CD    0   0
##      CSR   0   0
##      DC   11   1
##      GLD  21   1
##      IMS   5   0
##      ISR   2   0
##      LD    0   0
##      LT   17   0
##      MrD   0  18
##      MxD   2   0
##      SB   29   0
##      SD    0   0
##      SSR   0   0
##      TG  114   0
##      WB    0  80
## [1] "classification error rate with altdata:  0.739949109414758"
## [1] "Kappa overall =  0.201715644569542"
## [1] "Tau overall =  0.216524472384374"
## [1] "mean quality =  0.11783115442775"
## [1] "The quality is  0.11783115442775"
## Warning in chisq.test(CM): Chi-squared approximation may be incorrect
## [1] "#########  Cramer's V =  NaN"